Boolean Search Is Dead. Here's What Replaces It
Let me describe something that happens in recruiting offices every single day:
A recruiter opens LinkedIn Recruiter. They type something like ("software engineer" OR "software developer") AND ("React" OR "React.js") AND "AWS" NOT "intern". They get 47,000 results. They scroll through the first 3 pages. They InMail 50 people. They get 4 responses.
This is Boolean search. It's been the backbone of technical recruiting for 20 years. And it's completely, fundamentally broken.
The Math Problem With Boolean
Boolean search is keyword matching with logic operators. The problem: engineers don't describe themselves the way recruiters search for them.
You're looking for someone experienced with event-driven architectures. Your Boolean string includes "event-driven" AND ("Kafka" OR "RabbitMQ" OR "SQS").
You know who you just missed? The engineer whose profile says "Built real-time data pipeline processing 2M events/day using custom pub/sub system on GCP". That person is exactly who you want. But they didn't use your keywords.
For a typical senior backend engineering search, Boolean keyword matching finds about 35-40% of qualifying candidates. The majority of the talent pool is invisible.
What Actually Works: Semantic Search
Instead of matching keywords, match meaning.
When you type "senior backend engineer experienced with high-throughput event processing, preferably in fintech", a semantic search system understands the concepts:
- Senior maps to years of experience, title progression, technical depth
- Backend maps to server-side technologies, API design, databases
- High-throughput event processing maps to Kafka, message queues, streaming, AND people who described these things differently
- Fintech maps to companies in financial services, payment processing
At Candyfloss, we built our search this way from day one. You describe who you're looking for in plain English. Our system searches across millions of profiles, understanding what words mean, not just matching them.
The Compound Problem: Boolean + Bad Data
Across our database, we see 847 different ways people write "JavaScript". 23% of profiles list skills in their headline that don't appear anywhere else. 31% of senior engineers don't list specific technologies at all.
Boolean search can't handle this variation. Semantic search handles it natively.
What This Means in Practice
Boolean approach: 2,100 results, ~35 relevant after hours of manual review.
Semantic search on Candyfloss: 340 highly ranked results, ~42 relevant candidates in 45 minutes.
The semantic approach found more relevant candidates in less time because it identified people who described their experience in context rather than just people who typed the right keywords.
What Recruiters Should Do Right Now
Even with Boolean tools today: broaden your OR clauses aggressively, search for outcomes not just skills, and use signal data like job changes and GitHub activity.
Or skip all that and try a tool built for how search should work.