As the search landscape evolves with the rise of large language models (LLMs) and vector search, the future of search hinges on hybrid search, which combines traditional keyword search and contextual vector search. Despite the advancements in vector technology, keyword search remains essential for pinpointing tokens not covered by embedding models, leading to a blend of both search methods known as hybrid search. This approach was propelled by the needs of generative AI applications, prompting the development of fusion techniques like reciprocal rank fusion (RRF) and relative score fusion (RSF) to integrate results. The industry has adapted by embedding native hybrid search capabilities into platforms, enhancing developer efficiency and integration simplicity. The choice between lexical-first or vector-first solutions depends largely on the existing infrastructure, with considerations for indexing strategies and implementation styles influencing the decision. MongoDB exemplifies this evolution by incorporating vector search into its traditional search indexes, creating a robust platform that supports both operational and AI-driven use cases, and introducing native hybrid search functions for an integrated user experience.