A growing interest has been witnessed recently from both academia and\nindustry in building nearest neighbor search (NNS) solutions on top of\nfull-text search engines. Compared with other NNS systems, such solutions are\ncapable of effectively reducing main memory consumption, coherently supporting\nmulti-model search and being immediately ready for production deployment. In\nthis paper, we continue the journey to explore specifically how to empower\nfull-text search engines with fast and exact NNS in Hamming space (i.e., the\nset of binary codes). By revisiting three techniques (bit operation, subs-code\nfiltering and data preprocessing with permutation) in information retrieval\nliterature, we develop a novel engineering solution for full-text search\nengines to efficiently accomplish this special but important NNS task. In the\nexperiment, we show that our proposed approach enables full-text search engines\nto achieve significant speed-ups over its state-of-the-art term match approach\nfor NNS within binary codes.\n