For every reaction, the imine is categorised as either an E-or Z-imine, based on the sign of the recorded enantiomeric excess. Further, molecular descriptors, either physicochemical properties or topological, are calculated for all reaction partners. This data is used to develop a comprehensive model, finding that imine parameters govern the defining transition state and hence the preferred enantiomer. In a focused modelling, two separate models are constructed, one for all E-and Z-imines, respectively, finding substrate-catalyst matching is important for E-and Z-imines. The focused correlations enabled the authors to identify subtle mechanistic differences between reactions of E-and Z-imines, such as the role of steric and electronic properties of the imine for E-and Z-imines, respectively. The two-stage workflow, using the comprehensive model to distinguish the imine-type and subsequently using the focused model for detailed predictions, proved successful for out-of-sample reaction predictions with new nucleophiles, such as enecarbamates. Further, the authors also tested their models on the imine-additions published by Denmark and coworkers (see section 3.2), showcasing the importance of high-quality datasets for ML applications.