3) Training a model or testing a method requires not only information on highly selective catalysts, but also on poorly selective catalysts. For optimal accuracy, ML models should be trained to correlate "good" features (e.g., chemical groups) to high enantioselectivity, as well as "bad" features to poor enantioselectivity, while QM and MM methods must be evaluated for their ability to distinguish good from poorly stereoselective catalysts.