Exa has significantly optimized its BM25 index, a key component in traditional keyword search algorithms, by more than 50% across billions of documents without performance loss, enhancing the efficiency and cost-effectiveness of its AI-driven search engine. The optimization involves a hybrid search approach that combines keyword and embedding methods, leveraging advanced data structure techniques such as frequency-based organization, variable-length delta encoding, Zstd compression, and consolidated buffers to reduce memory overhead while maintaining rapid query performance. These improvements not only result in reduced infrastructure costs and faster system startup times but also enhance the quality of hybrid search results by improving the initial retrieval stage, ultimately aligning with Exa's mission to organize web data for complex queries and provide a seamless search experience.