The results of this study clearly indicate that current methods for parameterization are not descriptive enough to capture the factors that govern reaction success even when based on specific and relevant mechanistic insights. It must be noted that the success of different feature sets and models depends on the complexity of chemistry, the dimensionality of the design space and the number of variables. Given a different chemical design space with a larger number of ligands it might be possible that DFT-based descriptors start to outperform OHE because the number of OHE features increase whereas the number of descriptors stays constant. We believe that this work should serve as a challenge for the chemical community to discuss the trade-off between the development of more tailored parameterization methods or more exhaustive screening as two key factors for efficient reaction optimization.