Many algorithms for protein/peptide identification from Tandem Mass Spectrometry (MS/MS) data have been developed. The majority of such methods are based on sequence or spectral database search: experimental spectra are matched against spectra characterizing known peptide sequences. Hits define candidate peptides. In this context, the development of reliable and fast candidate peptide/spectrum filtering or ranking techniques would enhance the pre-processing of large scale datasets. The present work introduces IndiMax, a multi-stage, fast and accurate peptide/spectrum candidate filtering and ranking methodology for peptide MS/MS data. Based on a low complexity hierarchical search approach, IndiMax can be applicable to pre-process multiply charged (up to +5), de-isotoped, experimental MS/MS spectra of large quantity and variable quality. The developed methodology can be integrated into the general workflows to increase the identification rate and the consistency of the results. Thus, the potential user would get an opportunity to optimize the previously established data analysis strategy.