Boolean search is a sourcing technique that uses operators like AND, OR, and NOT to combine or exclude keywords, helping recruiters find qualified candidates faster.
Key Takeaways
Boolean search uses logical operators to create targeted search queries. In recruiting, it's how sourcers build precise strings that pull back exactly the right candidates.
Operators (AND, OR, NOT) plus punctuation (quotes, parentheses, asterisks) tell the search engine how to combine keywords. Without them, you get a flood of loosely related results.
70% of the workforce are passive candidates (LinkedIn). Boolean search finds them. Recruiters who use it cut sourcing time by 50%.
Each operator controls how the search engine processes keywords.
| Operator | What It Does | Example | When to Use |
|---|---|---|---|
| AND | Requires all terms present | "project manager" AND PMP AND construction | When candidates must have multiple qualifications |
| OR | Returns any of the terms | nurse OR "registered nurse" OR RN | For synonyms and title variations |
| NOT | Excludes results with a term | developer NOT junior NOT intern | To filter out irrelevant seniority levels |
| " " (Quotes) | Exact phrase search | "machine learning engineer" | For multi-word titles and skills |
| ( ) (Parentheses) | Groups terms together | (developer OR engineer) AND (React OR Angular) | When combining OR with AND terms |
| * (Asterisk) | Wildcard for word endings | manage* AND market* | To catch manager, management, managing, etc. |
Ready-to-use strings for common scenarios.
("software engineer" OR "software developer" OR "full stack") AND (Python OR Java) AND (AWS OR Azure) NOT intern NOT junior
("registered nurse" OR RN OR "nurse practitioner") AND (ICU OR "intensive care") AND (BSN OR MSN)
("account executive" OR "sales representative") AND (SaaS OR "B2B software") AND ("quota attainment" OR "revenue growth") NOT recruiter
("software engineer" OR developer) AND ("Women Who Code" OR "Out in Tech" OR NSBE OR SHPE) - searches for members of diversity organizations, compliant with EEOC guidelines
("product manager") AND ("Series B" OR "growth stage") AND fintech NOT "looking for" NOT "open to work"
Every platform handles operators slightly differently.
Supports AND, OR, NOT, quotes, parentheses. Doesn't support asterisk. Use built-in filters alongside Boolean. Free LinkedIn is more limited.
Indeed supports AND, OR, NOT, quotes, parentheses plus title: and company: modifiers. Other boards vary.
site:linkedin.com/in "data engineer" AND (Spark OR Hadoop). Free and often surfaces profiles LinkedIn's own search misses.
Search user profiles and code contributions. language:python location:"New York" followers:>50 finds active developers.
Techniques that surface candidates most recruiters miss.
Put Boolean groups inside other groups: ("front end" OR frontend) AND (React OR Vue) AND (senior OR lead) AND ("San Francisco" OR remote) NOT manager. Build incrementally and test after each layer.
Google's AROUND(n) operator finds terms within n words of each other. More precise than AND for X-ray searches.
Works on any website. site:github.com "machine learning" AND TensorFlow. Try meetup.com, conference speaker lists, alumni pages.
Small errors wreck results.
"software engineer" AND "software developer" returns zero results because nobody lists three titles. Use OR for synonyms.
project manager without quotes returns any page with 'project' and 'manager' separately.
Every NOT removes entire profiles, including seniors who mention they mentored a junior.
LinkedIn doesn't support asterisks. Google uses minus sign instead of NOT. Check syntax per platform.
Build incrementally. Start with role terms, add one layer at a time, test after each addition.
Habits of high-performing sourcers.
Shared doc with tested strings by role family. Teams with libraries report 30-40% faster sourcing (SourceCon).
Brainstorm every synonym and abbreviation before building any string. Can double your candidate pool.
Use filters for broad criteria (location, experience), Boolean for nuanced stuff (specific skills, exclusions).
Your first search is a draft. Check first 20-30 results, adjust one variable at a time. Typically 3-5 refinements.
Track which strings produce the best response rates and interview-to-hire ratios. Build a data-driven playbook.
Impact on sourcing performance.