One-hot encodings were used to represent the different combinations of reaction variables described above in the datasets. Both datasets consisted of the reaction yield of every combination of reaction variables. The distribution of reaction yields for all 4608 reactions in each dataset are displayed in Figure . A hard threshold of 20% yield was used to determine the label of each data point, i.e., one combination of reaction variables. A reaction with a yield above this threshold was labelled as a "successful" reaction, encoded as class 1. In the same way, a reaction with a yield below 20% was labelled as an "unsuccessful" reaction, encoded as class 0. The ratios of "successful" reactions are 54% and 65% for the Buchwald-Hartwig and Suzuki data set respectively. In order to evaluate the performance on these datasets, each dataset was randomly split into a training and test set consisting of 80% and 20% respectively, of all data. We evaluated the performance in terms of the area under the receiver operating characteristic curve (AUROC) with the goal of reaching the largest AUROC with the least number of labelled training data points from each dataset. To be robust against fluctuations in the predictions, we used a moving average to determine if an experiment reached the pre-defined desired AUROC. A run was determined to have reached the (desired) AUROC after 𝑘 queries if: