Despite the many advances in machine learning approaches for ligand-based virtual screening, few of these algorithms and pipelines have been validated with prospective chemical screens in comparison with appropriate baseline methods. Machine learning and virtual screening tools are now being applied to score and prioritize millions to billions of compounds more routinely. There are examples of experimentally evaluating those predictions, but they primarily employ structure-based virtual screening workflows.