TEA is a novel approach using contrastive learning to convert protein language model embeddings into a new 20-letter alphabet, enabling highly sensitive and efficient large-scale protein homology searches, without the need for structure. Search with TEA against Many (STEAM) performs on par with and complements structure-based methods without requiring any structural information, and with the speed of a sequence search.