While our workflow ultimately led to significant improvements in reaction performance, we were interested in evaluating what factors could lead to erroneous predictions as understanding the limitations of our models could inform future implementations of this workflow (Fig. ). We observed that ligands with significant prediction errors tended to be farther in chemical space from the training set ligands. For example, in the Hayashi-Heck reaction, Norphos L16 was poorly predicted by the selectivity model and was relatively remote from the training-set ligands (Fig. , graph 1). In contrast, the optimal ligand HexaMeO-BIPHEP (L12) was comparatively close in chemical space to several training-set ligands and its performance was accurately predicted. In fact, a plot of the distance of each ligand to the nearest training-set neighbor demonstrates a domain of model applicability-an allowable distance from the training set ligands within which the regression model is highly accurate. The concept of a domain of applicability is well-established in quantitative structure-activity relationship (QSAR) models and has previously been reported in reaction informatics. Generally, the uncertainty in a particular prediction is correlated to the similarity between that molecule and the molecules used to construct the model. Using Euclidean distance in chemical space to quantify the domain of applicability (see SI for details on distance calculation), ligands tested that were 0.5 normalized parameter-space units or less from a training-set ligand had a prediction mean absolute error (MAE) of 0.24 kcal/mol for the Hayashi-Heck reaction (Fig. , graph 2 and graph 3). In contrast, ligands with >0.5 normalized parameter-space units from a training-set ligand were poorly predicted (MAE = 1.7 kcal/mol). An extended domain of applicability was observed for the hydroformylation The successful multi-objective optimization of these two consecutive processes with different transition metal catalysts, distinct mechanisms, and training sets occupying different regions of chemical space highlights the utility of this workflow. We anticipate this strategy combined with the intuitive chemical space map of bidentate organophosphorus ligands has the potential to enable the development and of a wide array of transition-metal catalyzed reactions that employ these ligands. In order to extend these concepts to encompass all bidentate ligand classes, virtual libraries of P,N and N,N ligands are currently under construction and will be reported in due course.