Supervised machine learning approaches, including support vector machines, random forests, Bayesian classifiers, nearest-neighbor similarity searching, and a conceptually distinct mapping algorithm termed DynaMAD, have been investigated for their ability to detect structurally related ligands of a given receptor with different mechanisms of action. For this purpose, a large number of simulated virtual screening trials were carried out with models trained on mechanistic subsets of different classes of receptor ligands. The results revealed that ligands with the desired mechanism of action were frequently contained in database selection sets of limited size. All machine learning approaches successfully detected mechanistic subsets of ligands in a large background database of druglike compounds. However, the early enrichment characteristics considerably differed. Overall, random forests of relatively simple design and support vector machines with Gaussian kernels (Gaussian SVMs) displayed the highest search performance. In addition, DynaMAD was found to yield very small selection sets comprising only ~10 compounds that also contained ligands with the desired mechanism of action. Random forest, Gaussian SVM, and DynaMAD calculations revealed an enrichment of compounds with the desired mechanism over other mechanistic subsets.