The Open Mass Spectrometry Search Algorithm (OMSSA) is an efficient, sensitive, and specific algorithm designed for peptide identification in proteomics experiments. It uses a classical probability score to match experimental MS/MS spectra to sequences, similar to the statistical model used in BLAST. OMSSA filters noise peaks, extracts m/z values, and compares them to calculated m/z values from in silico digested peptides. The algorithm is faster than comparable algorithms and matches more spectra from a standard protein cocktail at default thresholds. OMSSA's scoring system is based on the probability of random matches, allowing for the setting of false positive rates. The algorithm includes a noise filter, precursor mass calculation, mass ladder comparison, and E-value calculation to improve sensitivity and specificity. OMSSA was validated against Mascot, a commonly used probability-based search algorithm, and showed better performance in terms of the number of spectra identified and faster processing time for large datasets. The ROC analysis further confirmed OMSSA's efficiency and specificity.The Open Mass Spectrometry Search Algorithm (OMSSA) is an efficient, sensitive, and specific algorithm designed for peptide identification in proteomics experiments. It uses a classical probability score to match experimental MS/MS spectra to sequences, similar to the statistical model used in BLAST. OMSSA filters noise peaks, extracts m/z values, and compares them to calculated m/z values from in silico digested peptides. The algorithm is faster than comparable algorithms and matches more spectra from a standard protein cocktail at default thresholds. OMSSA's scoring system is based on the probability of random matches, allowing for the setting of false positive rates. The algorithm includes a noise filter, precursor mass calculation, mass ladder comparison, and E-value calculation to improve sensitivity and specificity. OMSSA was validated against Mascot, a commonly used probability-based search algorithm, and showed better performance in terms of the number of spectra identified and faster processing time for large datasets. The ROC analysis further confirmed OMSSA's efficiency and specificity.