This study introduces a series of database filtering steps, HMMERHEAD, designed to reduce the search time for profile hidden Markov models (profile-HMMs) in large sequence databases. HMMERHEAD significantly reduces the search time by 20-fold for Forward and 6-fold for Viterbi algorithms, with minimal loss in sensitivity. An iterative profile-HMM search method, JackHMMER, is then implemented, which leverages HMMERHEAD to eliminate the need for subdatabase creation. On a benchmark, JackHMMER detects 14% more remote protein homologs than SAM's iterative method T2K and 28% more than NCBI's PSI-BLAST. The study demonstrates that heuristic database filtering can effectively speed up profile-HMM scoring while maintaining high sensitivity.This study introduces a series of database filtering steps, HMMERHEAD, designed to reduce the search time for profile hidden Markov models (profile-HMMs) in large sequence databases. HMMERHEAD significantly reduces the search time by 20-fold for Forward and 6-fold for Viterbi algorithms, with minimal loss in sensitivity. An iterative profile-HMM search method, JackHMMER, is then implemented, which leverages HMMERHEAD to eliminate the need for subdatabase creation. On a benchmark, JackHMMER detects 14% more remote protein homologs than SAM's iterative method T2K and 28% more than NCBI's PSI-BLAST. The study demonstrates that heuristic database filtering can effectively speed up profile-HMM scoring while maintaining high sensitivity.