The requirement for adequate initial assay data is a limitation of the supervised learning approach. However, once trained on sufficient data, these models can evaluate a new compound in milliseconds, making them attractive for very large libraries. Initial prospective evaluations of ligand-based models applied to large libraries have been promising but remain rare. In general, the performance of such models in practical applications with large synthesize-on-demand libraries is still poorly understood. Models that perform well in retrospective evaluations could be inaccurate in synthesize-on-demand libraries where the distribution of chemical structures is different from the training distribution.