Perhaps the greatest difficulty in interpreting large sets of protein identifications derived from mass spectrometric methods is whether or not to trust the results. For such experiments, the level of confidence in each protein identification made needs to be far greater than the often used 95% significance threshold to avoid the identification of many false-positives. To provide higher confidence results, we have developed an innovative scoring strategy coupling the recently published Average Peptide Score (APS) method with pre-filtering of peptide identifications, using a simple peptide quality filter. Iterative generation of these filters in conjunction with reversed database searching is used to determine the correct levels at which the APS and peptide quality thresholds should be set to return virtually zero false-positive reports. This proceeds without the need to reference a known dataset.