To address these challenges, we applied dataset design principles to effectively sample a diverse range of substrates and ligands, ensuring an adequate representation of the search space . HTE was deployed to produce reaction outputs, which were used in an active learning approach to reselect more efficient ligands . This iterative process led to a comprehensive dataset, which, in turn, served as the foundation for training a supervised machine learning model. The result was the development of a predictive and interpretable classification model establishing connections between coupling partners and ligands that promote successful reactions. Lastly, the intrinsic limitations and uncertainty of the model were leveraged to inform a ligand selection platform that can uncover unexpected success for challenging substrates. This is highlighted by the ability of the workflow to identify effective ligands for reactions predicted to have a low probability of success (Fig. ).