A set of guidelines has been developed for using the peptide hits technique (PHT) as a semi-quantitative screening tool for the identification of proteins that change in abundance in a complex mixture. The dataset that formed the basis for these experiments was created using a cell lysate derived from the yeast Saccharomyces cerevisiae, spiked at various levels with serum albumin (BSA), and analyzed by LC/MS/MS and SEQUEST. Knowing that the level of only one protein (BSA) actually changed in the mixture allowed for the development and refinement of the necessary bioinformatics and statistical analyses, e.g., principal component analysis (PCA), normalization, and analysis of variation (ANOVA). As expected, the number of BSA peptide hits changed in proportion to the amount of BSA added to the sample. PCA was able to clearly distinguish between the spiked samples and the untreated sample, indicating that PCA may be able to classify samples, e.g., healthy versus diseased, in future experiments. The use of an endogenous "housekeeping" protein was found to be superior to the use of total hits for data normalization prior to analysis. An ANOVA based model readily identified BSA as a protein of interest, that is, one likely to be changing from amongst the background proteins, indicating that an ANOVA model may be able to identify individual proteins in target or biomarker discovery experiments. General guidelines based on these combined observations are set forth for future analyses and the rapid screening for candidate proteins of interest.