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Tag: Boolean search

  • X-Ray Search for Recruiters: Google Strings That Still Work in 2026

    X-Ray Search for Recruiters: Google Strings That Still Work in 2026

    X-Ray Search for Recruiters: Google Strings That Still Work in 2026

    X-ray search for recruiters means using Google’s site: operator to search inside LinkedIn, GitHub, Stack Overflow and other sites from outside their own search boxes. For years it was the cheapest way to find passive candidates. Since early 2024 it has been less reliable on LinkedIn, but it still works well on developer sites.

    This guide gives you copy-paste strings, explains what changed, lists the mistakes that return empty results and shows when it is time to stop typing operators and use AI sourcing instead.

    TL;DR

    • X-ray search is a Google query pattern: site: plus quoted keywords, a location and exclusions.
    • It works best on GitHub and Stack Overflow. On LinkedIn it returns thinner results than it did before 2024.
    • Most failures come from small syntax errors: a space after site:, a lowercase OR or missing parentheses.
    • X-ray finds names, not contact details or outreach. Plan for a separate enrichment and outreach step.
    • Switch to AI sourcing when you run the same search across several platforms every week.

    Table of Contents

    What is X-ray search?

    X-ray search is a way to search a specific website through Google by adding the site: operator to a query. Instead of using LinkedIn’s search, you ask Google to return only pages from linkedin.com/in that match your keywords. The same idea works for GitHub profiles, Stack Overflow user pages, university sites and public PDF resumes.

    It is a form of Boolean search. If you need a refresher on operators like AND, OR and NOT, start with our guide to Boolean search for recruiters. X-ray adds one idea on top: the site restriction.

    The basic formula is:

    site:[domain or path] "keyword 1" "keyword 2" "location" -exclusion

    Google operators recruiters use

    Google’s own search help page documents fewer operators than recruiters use in practice, so test each string before you rely on it. These are the ones that matter:

    Operator What it does Example
    site: Limits results to a domain or path site:github.com
    " " Matches an exact phrase "machine learning"
    OR Matches either term (use capitals) ("Django" OR "FastAPI")
    - Excludes a term -jobs -directory
    intitle: Term must appear in the page title -intitle:repositories
    inurl: Term must appear in the URL -inurl:status
    filetype: Finds a file type such as PDF filetype:pdf "resume"
    * Wildcard for a missing word "senior * engineer"

    Some old operators no longer work. Pin’s X-ray search guide reports that Google retired related: in 2023 and cache: in 2024. If a string copied from an old blog post returns nothing, an outdated operator is a likely cause.

    Copy-paste X-ray strings

    Swap the bracketed parts for your own role, skills and location. These patterns are adapted from the examples in Pin’s guide and 100Hires’ LinkedIn X-ray guide.

    GitHub: developers by skill and city

    site:github.com "Python" ("Django" OR "FastAPI") "Berlin" -intitle:repositories

    The -intitle:repositories exclusion removes repository listing pages, so you see more user profiles. Our full walkthrough on finding passive candidates beyond LinkedIn covers what to do once you land on a profile.

    Stack Overflow: people who answer questions in your stack

    site:stackoverflow.com/users "java" "toronto"

    User pages show the topics someone answers about, which is useful evidence of depth in a specific technology.

    LinkedIn: public profiles (reduced results)

    site:linkedin.com/in ("software engineer" OR "backend engineer") "austin" -jobs -directory

    Expect fewer and less complete results than the same search in LinkedIn Recruiter. More on why below.

    Public resumes: PDF files

    filetype:pdf "software engineer" "Python" "Chicago" -jobs -template -sample

    The exclusions remove job ads and resume templates. Resumes found this way can be years old, so check the candidate’s current role before you reach out.

    University project pages

    site:*.edu filetype:pdf "resume" "computer science" "machine learning"

    Useful for early-career roles and research-heavy teams.

    How to build your own string in five steps

    1. Pick the platform. Choose where your candidates leave public evidence. Engineers leave code on GitHub and answers on Stack Overflow. Designers and marketers are more likely to appear on portfolio sites.
    2. List the title variants. Candidates write “Software Engineer”, “SDE” or “Backend Developer”. Put the variants in one parenthesis group joined with OR.
    3. Add two or three skills. Use the terms a candidate would write on their own profile, not the wording from your job post.
    4. Add a location. A city name filters well. For remote roles, leave it out and filter later.
    5. Exclude noise. Add -jobs, -intern or -recruiter one at a time and check the results after each change.

    Why LinkedIn X-ray got worse

    Sourcing experts documented a change in early 2024: Google began indexing fewer fields from public LinkedIn profiles. According to Pin’s guide, Google can no longer see work history, education, skills or most location data on many profiles. A search that once matched a skill in the middle of a profile now only matches the headline and a few visible fields.

    Public profiles can also take weeks or months to refresh in search engines, so a title you see in Google may be out of date. LinkedIn also does not publish a feed of changes for Google to follow. Treat every X-ray result as a lead to check, not as a confirmed current profile.

    If you also pay for LinkedIn Recruiter, see our comparison of LinkedIn Recruiter alternatives for cheaper options.

    Common mistakes that return empty results

    • A space after the colon. site: github.com breaks the operator. Write site:github.com.
    • Lowercase OR. Use OR in capitals.
    • Missing parentheses. Without them, OR can scramble the logic of the whole query.
    • Job-post language. “Seeking an experienced engineer” is not how people describe themselves. Search for the terms they actually write.
    • Too many exclusions. Each minus term can hide real candidates. Add them gradually.
    • Typos in the path. linkedin.com/il returns nothing. The profile path is /in.

    Long strings can also be cut short. 100Hires reports that Bing limits queries to 10 terms, so the end of a long string may be ignored. Keep strings short and test in Google first.

    Limits and compliance

    X-ray search is useful, but it has real limits:

    • Exact-match dependence. It misses people who describe the same skill in different words.
    • One platform at a time. You run, review and de-duplicate each site’s results by hand.
    • Partial coverage. Pin’s own index analysis, a vendor figure, suggests fewer than 13% of software engineering profiles have a public GitHub. Many strong engineers never appear in a GitHub X-ray.
    • No contact data and no workflow. You get a name and a link. Email lookup, outreach and tracking are manual. Read our guide on recruiter outreach response rates before you start messaging.

    Public does not mean free to use however you like. Check the terms of each platform you search, and follow privacy rules such as GDPR if you store candidate data. Ask your legal or compliance contact before you build a database from scraped results.

    X-ray search vs Boolean search vs AI sourcing

    X-ray search Boolean in a sourcing tool AI sourcing
    Where you search Google, one site at a time Inside one platform Across a profile database
    How you query Operators and exact phrases Operators and filters Plain-language criteria
    Handles synonyms Only if you list them Only if you list them Usually, by design
    Contact data No Sometimes Often through enrichment
    Cost Free Tool subscription Tool subscription
    Best for Niche, one-off searches Precise control Repeat searches at volume

    For a deeper look at the trade-off, read our comparison of Boolean search vs natural language search.

    When to switch from X-ray to AI sourcing

    Keep X-ray in your toolkit for rare skills and quick checks. Move to an AI sourcing tool when:

    • you repeat similar searches every week for different roles;
    • you spend more time cleaning results than contacting people;
    • you need email addresses and a place to track replies; or
    • your candidates are not on GitHub or Stack Overflow.

    ConnectDevs Scout searches a published 800M+ public-profile graph and returns ranked evidence for recruiter review. Missing emails can be filled in with candidate enrichment. Plans start free, with paid tiers listed on the pricing page. You still review every shortlist yourself.

    FAQ

    What is X-ray search in recruiting?

    X-ray search is a Google search that uses the site: operator to find profiles on one website, such as GitHub or LinkedIn, using keywords, locations and exclusions.

    Does LinkedIn X-ray search still work in 2026?

    It still returns some public profiles, but results are thinner than before 2024 because Google indexes fewer profile fields. Use it for leads and verify them.

    Is X-ray search free?

    Yes. Google search costs nothing. Your costs are time and the tools you need for email lookup and outreach.

    Is X-ray search against LinkedIn’s rules?

    Searching Google for public pages is different from scraping LinkedIn. Automating collection or copying profile data at scale can breach platform terms and privacy law. Check the current terms and ask your legal team.

    Why did my X-ray search stop working?

    The usual causes are a retired operator, a syntax error such as a space after site:, too many exclusions, or Google’s reduced LinkedIn indexing.

    Can AI write X-ray search strings?

    Yes, a chat assistant can draft strings quickly. Test each one, because assistants sometimes use retired operators or break the parentheses.

    Conclusion

    X-ray search is still worth learning. It costs nothing, it works well on GitHub and Stack Overflow, and it teaches you how candidates describe themselves. Use it for narrow, one-off searches, and accept that LinkedIn results are now partial. When the same work repeats weekly, a tool that searches intent instead of exact strings will save you the hours.

    Tired of rebuilding strings for every role? Explore AI candidate sourcing and run your next search on a live role.

  • Boolean Search for Recruiters: Strings, Examples, and When to Switch to AI

    Boolean Search for Recruiters: Strings, Examples, and When to Switch to AI

    Boolean search for recruiters is still the fastest way to turn a vague job brief into a precise list of profiles, as long as you know the operators, the traps, and where the method stops working. This guide covers the syntax, ready-to-use strings for common engineering and non-technical roles, and a clear test for when to move part of your sourcing to natural-language AI search.

    TL;DR

    • Five operators do almost all the work: AND, OR, NOT, quotation marks, and parentheses. On LinkedIn the operators must be uppercase.
    • Build every string in three blocks: title synonyms, must-have skills, exclusions. Wrap each block in parentheses.
    • Boolean can only find the words you predicted. Candidates who describe their work differently stay invisible, and you never see them.
    • Use Boolean when the role has stable, standard vocabulary. Use natural-language search when the skills are new, niche, or described in many different ways.
    • Test any string on a small result set first. If the top 20 profiles are mostly wrong, fix the string before you send a single message.

    Table of contents

    Boolean search is a way of combining keywords with logical operators so a search engine returns only the profiles that satisfy your conditions. Recruiters use it in LinkedIn, in applicant databases, and in Google to find candidates by job title, skill, location, or employer. The name comes from Boolean logic, where every condition is either true or false.

    A plain keyword search for python developer returns everyone who mentions either word anywhere. A Boolean string such as ("python developer" OR "python engineer") AND (Django OR FastAPI) NOT intern returns only people matching the exact phrases you chose, with the exclusions applied.

    The five operators you actually need

    Most of the long cheat sheets online list a dozen operators. Five cover nearly every real search, and these are the ones LinkedIn’s search box reliably understands.

    Operator What it does Example
    AND Requires both terms "data engineer" AND Spark
    OR Accepts either term, used for synonyms "site reliability engineer" OR SRE
    NOT Removes profiles containing a term "backend developer" NOT intern
    ” “ Matches an exact phrase "machine learning engineer"
    ( ) Groups terms so the logic runs in the order you intend (Python OR Go) AND Kubernetes

    Two syntax rules cause most failed searches. First, AND, OR, and NOT generally need to be uppercase on LinkedIn, otherwise they are read as ordinary words. Second, wildcards such as develop* are not supported in LinkedIn search, so list the variants you want (developer OR development) instead. LinkedIn also changes search behavior by plan and product, so confirm the details in your own account’s help documentation before you build a saved search around an edge case.

    How to build a Boolean string in three blocks

    A string that grows one keyword at a time becomes unreadable fast. Build it in three blocks, then combine them.

    1. Titles block. List every realistic title for the role inside one set of parentheses, joined with OR. For a platform role: ("platform engineer" OR "infrastructure engineer" OR "devops engineer" OR "site reliability engineer" OR SRE).
    2. Skills block. List the one or two must-have skills, each with its common variants: (Kubernetes OR K8s) AND (Terraform OR Pulumi). Keep this block short. Every extra AND removes people who may be a strong fit but phrase things differently.
    3. Exclusions block. Add NOT terms only for noise you have actually seen in the results: NOT (intern OR student OR recruiter).

    Combine them with AND between blocks. Then run the search and read the first 20 results. If more than a quarter are clearly wrong, the usual cause is a title that is too generic or a missing exclusion. Change one block at a time so you know what fixed it.

    Copy-paste Boolean strings

    These are starting points. Adjust the synonyms to the stack and seniority your hiring manager describes.

    Backend engineer

    ("backend engineer" OR "backend developer" OR "software engineer")
AND (Go OR Golang OR Java OR Python)
AND (microservices OR "distributed systems")
NOT (intern OR student OR junior)

    Platform or DevOps engineer

    ("platform engineer" OR "devops engineer" OR "site reliability engineer" OR SRE)
AND (Kubernetes OR K8s)
AND (Terraform OR Pulumi OR CloudFormation)
NOT (intern OR student)

    Machine learning engineer

    ("machine learning engineer" OR "ML engineer" OR "applied scientist")
AND (PyTorch OR TensorFlow)
AND (deployment OR MLOps OR "model serving")
NOT (intern OR student)

    Sales development or account executive

    ("account executive" OR "sales executive" OR "enterprise sales")
AND (SaaS OR B2B)
AND (quota OR pipeline OR "closed won")
NOT (intern OR assistant)

    Technical recruiter (for agencies hiring their own)

    ("technical recruiter" OR "tech recruiter" OR "technical sourcer")
AND (engineering OR software OR "IT staffing")
NOT (coordinator OR intern)

    Notice that none of these strings tries to be clever. They use plain titles, two or three skills, and a short exclusion list. Strings with fifteen nested conditions usually return fewer candidates, not better ones.

    Google X-ray search for profiles outside your tool

    X-ray search uses Google’s site: operator to search a specific website’s public pages, which lets you look for candidates on sites where you have no recruiter license. The pattern is site:[domain] "job title" "skill" "location". For example:

    site:github.com "machine learning" (Python OR PyTorch) "Berlin"

    Google uses a minus sign for exclusions, so write -jobs -careers rather than NOT. X-ray results are public pages only, so treat them as leads and verify details before outreach. For more places to look beyond a single network, see our guide to finding passive candidates beyond LinkedIn.

    Common Boolean mistakes

    • Missing parentheses. Python OR Java AND AWS does not mean what most people think it means. Group the OR terms: (Python OR Java) AND AWS.
    • Too many AND conditions. Each one shrinks the pool. Start with two skills, not six.
    • Searching only the title you were given. The hiring manager’s job title is rarely the title candidates use. Include at least four synonyms.
    • Copying strings from the internet unchanged. A string tuned for another company’s stack will return their candidates, not yours.
    • Never testing exclusions. A careless NOT can remove the very people you want, for instance excluding “manager” while hiring an engineering manager.

    Where Boolean search breaks down

    The core limit is not syntax. It is vocabulary. A Boolean string can only match words the recruiter predicted in advance. As one sourcing vendor puts it, a keyword search only reaches the vocabulary you predicted, and strings cannot show you who they missed, only who they matched (Metaview). An infrastructure engineer who writes “self-managed clusters” on their profile instead of “Kubernetes” will never appear for a string built around Kubernetes.

    That matters most in four situations:

    1. New or fast-changing skills. Titles and terms for AI and platform roles shift every few months, so strings go stale.
    2. Niche combinations. “Someone who has scaled a payments system in a regulated market” is a concept, not a keyword.
    3. Non-standard careers. Founders, freelancers, and people who moved between industries describe their work in their own words.
    4. Volume. Writing, testing, and maintaining strings for ten open roles is real recruiter time that does not go into talking to candidates.

    A caveat applies to the AI side too. Vendor benchmark claims for AI sourcing are largely vendor-run, and even Metaview acknowledges nobody has yet measured whether AI-surfaced candidates are better hires than those a skilled sourcer would have found. Judge any tool, including ours, on the quality of the shortlist it gives you on your own roles.

    Boolean vs. natural-language search: how to choose

    Natural-language search lets you describe the person you want in plain sentences, and a semantic model finds profiles that match the meaning rather than the exact words. For a deeper look at why meaning-based matching differs from keyword matching, read our piece on intent-based matching vs. keyword search.

    Situation Better fit Why
    Standard title, stable skills (for example, Java developer) Boolean Vocabulary is predictable, results are fast and controllable
    Niche or emerging skills Natural language Candidates describe the work in varied terms
    Brief is a story, not a keyword list Natural language Concepts and context do not reduce to operators
    You need an auditable, repeatable search Boolean The logic is explicit and can be saved and shared
    Many open roles, small team Natural language Less time spent writing and tuning strings

    Most strong sourcers end up using both. They run a tight Boolean search for the obvious pool, then run a natural-language search on the same brief to find the candidates the string missed. Compare the two lists. The profiles that appear only in the second list tell you which synonyms your string was blind to, and you can add them back.

    ConnectDevs Scout is built for the second half of that workflow. You describe the role in plain language, and Scout’s AI candidate sourcing converts it into a structured search over a public-profile graph, ranks the results, and shows why each person matched so you can check the reasoning instead of trusting a score. It supports recruiter judgment and does not replace it, and it does not guarantee a candidate’s availability or interest.

    Whichever method finds the profile, the next problem is missing contact details and stale data. That is where candidate enrichment fits in, and if you are weighing tools more broadly, our comparison of LinkedIn Recruiter alternatives covers pricing and trade-offs.

    FAQ

    What is Boolean search for recruiters?

    Boolean search for recruiters is a method of combining keywords with the operators AND, OR, NOT, quotation marks, and parentheses to filter candidate profiles precisely. It lets you specify titles, skills, and exclusions in a single query instead of scanning results by hand.

    Does LinkedIn support Boolean search?

    Yes. LinkedIn supports AND, OR, NOT, quotation marks, and parentheses in keyword search, and operators should be typed in uppercase. Wildcards are not supported. Exact limits and field behavior vary by plan, so check the help documentation for your LinkedIn product.

    What is an X-ray search?

    An X-ray search uses Google’s site: operator to search the public pages of a single website, such as GitHub or a company team page, for candidate information. It is useful for finding leads outside a paid recruiting tool, though results are limited to public pages.

    Is Boolean search still worth learning in 2026?

    Yes. It is fast, transparent, and effective for roles with standard titles and skills, and the logic carries over to applicant databases and ATS filters. It works best alongside natural-language search, which covers the vocabulary Boolean cannot predict.

    How long should a Boolean string be?

    As short as it can be while still returning relevant profiles. Two or three skills plus a title block and a short exclusion list is usually enough. If you keep adding conditions to fix results, the problem is probably the title synonyms, not the skill list.

    What is the difference between Boolean and natural-language candidate search?

    Boolean matches the exact words and logic you specify. Natural-language search interprets a plain-English description and matches on meaning, so it can find profiles that use different wording. Boolean is more controllable; natural language is broader.

    Conclusion

    Learn the five operators, build strings in three blocks, and test them on a small sample before you commit to outreach. Keep Boolean for roles with predictable vocabulary, and add natural-language search wherever your strings keep missing people. If you want to see how a plain-language brief performs against your current strings on a live role, book a demo and bring a brief you have already sourced for.

  • Boolean Search vs Natural Language Search: How Recruiters Should Source in 2026

    Boolean Search vs Natural Language Search: How Recruiters Should Source in 2026

    Boolean Search vs Natural Language Search: How Recruiters Should Source in 2026

    Most recruiters learned Boolean search the hard way: a string that returns 40,000 profiles, then a tighter one that returns six. The Boolean search vs natural language search debate is now practical, not theoretical, because LinkedIn and most AI sourcing tools let you type a plain-English description of the person you want. This guide shows where Boolean still wins, where natural language wins, and a workflow that uses both, with real examples for technical roles.

    TL;DR

    • Boolean search matches exact keywords and operators. It is precise and predictable, but it misses candidates who describe themselves differently.
    • Natural language search interprets intent, related titles, and adjacent skills. It finds more relevant people faster, but you must check why each result matched.
    • Use Boolean when you need exact, auditable filters. Use natural language to explore and to find people with unusual titles.
    • The best workflow starts with a plain-language search, reads the results, then adds Boolean constraints only where precision matters.

    Table of Contents

    What Is Boolean Search in Recruiting?

    Boolean search is a way of searching by combining keywords with logical operators such as AND, OR, and NOT, plus quotation marks for exact phrases and parentheses for grouping. A recruiter writes the rules, and the database returns only profiles that satisfy them literally.

    That literal behavior is both its strength and its weakness. If you write ("Software Engineer" OR "Software Developer") AND (Go OR Golang), you get profiles containing those words. You do not get the engineer whose title is “Member of Technical Staff” or “Backend Developer” unless you thought to include them. Good Boolean work is mostly the craft of anticipating every way a candidate might describe themselves.

    What Is Natural Language Candidate Search?

    Natural language candidate search lets a recruiter describe the person they want in plain English, such as “backend engineer with production Go experience at an early-stage startup, three to five years in.” The system interprets the request, maps it to related titles, skills, seniority, and career context, and returns ranked results.

    The idea is no longer niche. LinkedIn announced AI-assisted search for Recruiter in October 2023, letting recruiters describe a hiring need and receive recommendations from a broader pool. HR Dive reported that early testing showed 74% of recruiters saw time savings. That is a vendor-reported early-testing figure, so read it as a signal of direction rather than a benchmark. Sourcing platforms such as ConnectDevs work the same way: AI candidate sourcing converts role requirements into a structured search and shows the reasoning behind each result for recruiter review.

    Side-by-Side Comparison

    Factor Boolean search Natural language search
    Input Keywords and operators Plain-English description
    Matching Exact terms only Related titles, skills, and context
    Learning curve Steep; skill takes practice Low; usable by hiring managers and founders
    Predictability High; same string, same rules Lower; ranking depends on the model
    Auditability Easy to explain exactly why a profile matched Depends on whether the tool shows its reasoning
    Risk Missing good candidates with different wording Including loosely relevant candidates
    Best for Narrow, well-defined searches and compliance-sensitive filters Exploring a market and roles with inconsistent titles

    Examples for a Backend Engineer Search

    Boolean version

    ("Backend Engineer" OR "Software Engineer" OR "Platform Engineer") AND (Go OR Golang) AND ("distributed systems" OR microservices) NOT (recruiter OR sourcer)

    This works if candidates use those exact words. It excludes anyone whose profile says “Staff Engineer, payments infrastructure” without the phrase “distributed systems.”

    Natural language version

    Backend engineer who has shipped production Go services at a startup or scale-up, comfortable with distributed systems, three to six years of experience, based in Europe.

    A semantic system can surface the payments infrastructure engineer above because it understands the skills overlap. You still need to open profiles and confirm the evidence.

    Combined version

    Run the plain-language search first. Scan the top results and note the job titles and skill words that appear. Then add a Boolean filter only for hard requirements, such as a work-authorization region or a specific technology you cannot compromise on.

    A Workflow That Uses Both

    1. Write the requirement as a paragraph. Include the outcome the person will own, not only a title.
    2. Run a natural language search and review the first 20 to 30 results for relevance.
    3. Harvest vocabulary. Note titles and skill terms strong candidates use. This is also how you improve future Boolean strings.
    4. Add hard filters with Boolean or structured filters for location, seniority, and must-have skills.
    5. Check the evidence. Confirm the reason each candidate matched before you send outreach.
    6. Enrich the shortlist so work history and contact routes are complete. Our guide to candidate enrichment explains why this step saves outreach time.

    For more ways to reach engineers who never update a profile, see our tactics for finding passive candidates beyond LinkedIn, and read why intent-based matching outperforms keyword search for the reasoning behind the shift.

    Mistakes to Avoid

    • Trusting the ranking blindly. A ranked list is a starting point. Semantic tools can rank a loosely related profile highly.
    • Over-specifying. Long plain-language prompts stuffed with every preference can narrow results the same way a bad Boolean string does.
    • Dropping Boolean entirely. For regulated or audited searches, exact filters are easier to document and defend.
    • Ignoring bias. Any filter, keyword or semantic, can screen out people unfairly. Review the composition of results and keep a human in the decision.

    FAQ

    What is the difference between Boolean search and natural language search?

    Boolean search matches the exact keywords and operators a recruiter writes. Natural language search accepts a plain-English description and interprets meaning, including related titles and skills, to return ranked candidates.

    Is Boolean search still worth learning in 2026?

    Yes. It gives precise, explainable filters, works in nearly every database, and helps you check what an AI tool returned. It is now one tool among several, not the only way to search.

    Is natural language search more accurate than Boolean?

    Not automatically. It usually finds more relevant people with unusual titles, but Boolean is more predictable for narrow searches. Accuracy depends on the tool and on how well you review the results.

    Can natural language search replace recruiters?

    No. It speeds up discovery and research. Judging fit, motivation, and context, and making the hiring decision, remain human responsibilities.

    Does LinkedIn Recruiter support natural language search?

    LinkedIn introduced AI-assisted search in Recruiter in 2023 and has continued to add AI features. Availability depends on your license, so check your current plan. See our list of sourcing alternatives if you want to compare options.

    How do I write a good natural language search for engineers?

    Describe the work the person will do, the stack, the seniority, and the type of company they should have worked in. Avoid listing every nice-to-have. Start broad, review results, then narrow.

    Conclusion

    Treat Boolean and natural language search as complementary. Start with intent to discover people you would not have found with keywords, use what you learn to sharpen your filters, and verify every shortlisted profile. The recruiters getting the best results are not choosing a side; they are using each method for the part of the search it does best.

    Want to see a plain-English search across an 800M+ public-profile graph? Start sourcing candidates with ConnectDevs, or start hiring free and run your next search today.