The data gained from running this experiment can be used to re-train the ML model and new predictions are made. This iterative process continues until the desired objective (e.g., more accurate predictions, increased yield) is fulfilled. ML-based optimizers have the potential to increase the efficiency in which chemical space is navigated, removing operator bias and ultimately reducing the total number of experiments required, thus reducing waste significantly. Previous reports vary in their conclusions on whether the implementation of chemical descriptors, rather than generic one-hot encoding (OHE) representations, truly boosted the predictive performance of their ML models. Pd-catalyzed C(sp 3 )-H activation is a powerful reaction manifold that enables the facile introduction of functional complexity in small molecules, in addition to the late-stage functionalization of complex molecules like trimipramine (XX), a tricyclic antidepressant, as recently demonstrated by one of our groups. Hence we chose to explore the parameterization and featurization of the newly developed tertiary amine directed C(sp 3 )-H bond activation with a HTE-generated dataset, comparing tailored descriptors, based on in silico studies, to understand the influence of descriptor complexity on supervised ML prediction and closed-loop optimization.