To compare the performance of different ML models on the given dataset within an interpolative task, the existing data was split randomly into a training (80%) and test (20%) set and the evaluation was repeated six times to generate mean and standard deviation values. This was conducted for each data representation and the mean and standard deviation values were calculated (Figure ). More complex features (that were specifically generated using prior knowledge of the reaction) such as DFT and fingerprint-derived descriptors delivered only marginal increases in performance compared to OHE which represents the baseline. RF and GP demonstrated almost equal prediction performance, regardless of which features were used. OHE along with a linear model delivered an RMSE of 9.6% ± 0.7% (standard deviation) yield. The best performance was achieved with feature set 2 and 14 using RF, giving an RMSE of 7.6% ± 1.2% and 7.2% ± 1.3% respectively. As visible in Figure , feature set 14 is a combined input of steric and electronic ligand DFT descriptors (Sterimol, % buried volume, NBO, CHELPG), principal components of the Morgan 2 fingerprints, OHE and existence/absence of a proton on the amide nitrogen. RMSE is reported with respect to yield -between 0 and 1. Figure shows that different features influence the performance of ANN and the linear model significantly and that their overall performance is worse than RF or GP, which is likely due to the small size of the dataset or choice of the features. Figure illustrates a parity plot of RF regression using feature set 14, illustrating the fitting of the training and test data.