In recent years, the generation and interpretation of MS/MS spectra for the identification of peptides and proteins has matured to a frequently used automatic workflow in Proteomics. Several software solutions for the automated analysis of MS/MS spectra allow for high-throughput/high-performance analyses of complex samples. Related to MS/MS searches, target-decoy approaches have gained more and more popularity: in a "decoy" part of the search database nonexistent sequences mimic real sequences (the "target" sequences). With their help, the number of falsely identified peptides/proteins can be estimated after a search and the resulting protein list can be cut at a specified false discovery rate (FDR). This is an essential prerequisite for all quantitative approaches, as they rely on correct identifications. Especially the label-free approach "spectral counting"-gaining more and more popularity due to low costs and simplicity-depends directly on the correctness of peptide-spectrum matches (PSMs). This work's aim is to describe five popular search engines-especially their general properties regarding protein identification, but also their quantification abilities, if those go beyond spectral counting. By doing so, Proteomics researchers are enabled to compare their features and to choose an appropriate solution for their specific question. Furthermore, the search engines are applied to a spectrum data set generated from a complex sample with a Thermo LTQ Velos OrbiTrap (Thermo Fisher Scientific, Waltham, MA, USA). The results of the search engines are compared, e.g., regarding time requirements, peptides and proteins found, and the search engines' behavior using the decoy approach.