When using a random split of the data for training/testing partitions, we found that simple OHE delivered already high-quality predictions for reaction yield, however, by adding more complex chemical descriptors we achieved slightly lower prediction error. For out-of-sample predictions we learned that neither fingerprints nor complex DFT-derived descriptors delivered significantly better performance compared to OHE, even though the descriptors were chosen based on mechanistic insights.