Comparing the yield prediction performance of DRFP to that of learned and physical descriptor-based fingerprints shows that this simple fingerprint is competitive, as it demonstrates consistent performance on all test sets. Averaging the 11 tests shown in Table , and). In order to predict reaction yields using DRFP, gradient boosting with early stopping was chosen as a machine learning technique. 10% of each training split was set aside and used to evaluate for early stopping. Hyperparameter optimisation was performed on five random splits (70/30). The resulting performance (R 2 ) is then compared to the density functional theory (DFT) based fingerprint with a random forest regressor by Ahneman et al. , Yield-BERT, an extension of the learned rxnfp fingerprint with a regression layer, and an augmented variant of the latter (Table ). The data set used is a collection of 3,955