How one navigates this traditionally difficult multi-optimization problem has inspired us to develop a workflow to synchronously improve yield and selectivity by sequentially using ML tools to analyze experimental data (Fig. ). The workflow consists of three key stages: (1) use of classification algorithms to assess reactivity (i.e., conversion or yield), (2) MLR to assess selectivity (regio-and/or stereoselectivity), and (3) virtual ligand screening to predict both reactivity and enhanced selectivity. Ultimately, this process de-risks subsequent experimental testing of extrapolations by modelling multiple objectives and through the use of a chemical space analysis technique to understand the domain of applicability for a given model. This study encompasses the culmination of multiple data-science tools and molecular featurization techniques that have been developed by our group and others.