Molecular networking (MN) has become a popular data analysis method for untargeted mass spectrometry (MS)/MS-based metabolomics. Recently, MN has been suggested as a powerful tool for drug metabolite identification, but its effectiveness for drug metabolism studies has not yet been benchmarked against existing strategies. In this study, we compared the performance of MN, mass defect filtering, Agilent MassHunter Metabolite ID, and Agilent Mass Profiler Professional workflows to annotate metabolites of sildenafil generated in an in vitro liver microsome-based metabolism study. Totally, 28 previously known metabolites with 15 additional unknown isomers and 25 unknown metabolites were found in this study. The comparison demonstrated that MN exhibited performances comparable or superior to those of the existing tools in terms of the number of detected metabolites (27 known metabolites and 22 unknown metabolites), ratio of false positives, and the amount of time and effort required for human labor-based postprocessing, which provided evidence of the efficiency of MN as a drug metabolite identification tool.