Our aim was to determine systems where easily understood chemical features show which substituents within the ligand(s) used are likely to lead to selectivity improvement. We chose to compare, a bespoke steric/electronic feature set against one developed for medicinal chemistry and against our recently developed Graph Neural Network approach (HCat-GNet). All these ML models were asked to predict the product enantioselectivities of the same data set of Pd-catalyzed α-aryl DAAA reactions and asked to highlight the areas of the substrate/ligand that are key to the success of the reaction.