The MOSES benchmark introduced another standardised training set and comparison between generative models. Although this was not applicable to goal-directed learning and only aimed at distribution-learning (i.e., how representative de novo molecules are of the respective training molecules), this benchmark proposed a useful suite of performance metrics to evaluate de novo molecules, all of which are integrated into MolScore. Docking benchmarks such as the smina-docking-benchmark (against four protein targets), DOCKSTRING (against three targets) and a similar docking benchmark in the Therapeutic Data Commons (against one protein target) have also emerged. However, caution should be used when using docking score alone to rank generative models, as optimising docking score can lead to large and/or greasy molecules being generated which are not desirable in a medicinal chemistry context, as observed in DOCKSTRING single docking task. Thus, it is likely that a purely docking benchmark will rank highly generative models that are unregularised or can generate out-of-domain molecules, instead of those useful in practice. Moreover, none of the above benchmarks conduct full ligand preparation which should consist of protonating molecules at a biologically relevant pH, enumerating unspecified stereoisomers (which are present in many generative models) or enumerating tautomersall necessary steps taken in virtual screening pipelines. MolScore contains similar functionality to conduct docking via interaction with a variety of docking software (including Smina), but crucially also contains some appropriate ligand preparation protocols that handle stereoisomer numeration, tautomer enumeration and protonation states.