The cytochrome P450 (CYP) superfamily represents the major enzyme class responsible for the metabolism of exogenous compounds. Investigation of clearance pathways is therefore an integral part in early drug development, as any alteration of metabolic enzymes may markedly influence the toxicological profile and efficacy of novel compounds. In silico methods are widely applied in drug development to complement experimental approaches. Several different tools are available for that purpose, however, for CYP enzymes they have only been applied retrospectively so far. Within this study, pharmacophore- and shape-based models and a docking protocol were generated for the prediction of CYP1A2, 2C9, and 3A4 inhibition. All theoretically validated models, the validated docking workflow, and additional external bioactivity profiling tools were applied independently and in parallel to predict the CYP inhibition of 29 compounds from synthetic and natural origin. After subsequent experimental assessment of the in silico predictions, we analyzed and compared the prospective performance of all methods, thereby defining the suitability of the applied techniques for CYP enzymes. We observed quite substantial differences in the performances of the applied tools, suggesting that the rational selection of that virtual screening method that proved to perform best can largely improve the success rates when it comes to CYP inhibition prediction.