Aryl It can be observed that the bases and ligands for Buchwald-Hartwig seem to have a relatively high frequency of retaining a label across variations, as does the reagent for the Suzuki reaction in the studied space. This cannot explain the whole nature of interactive effects for these parameters, as they are not independent. For the random forest model, the feature importance was analysed for both datasets with models trained with 1500 labelled data points, which were obtained by using 10 initially labelled data points and utilizing Margin to query labels, see Table . This analysis shows that while the more heterogenous features were directly related to a higher importance to the model predictions in the Buchwald-Hartwig reactions, the same was not true for the Suzuki counterparts. Here the ligand was overwhelmingly the most important factor for the model outcome, and the other four features played a smaller role, although the reactants do stand out as being of the least importance to the model. To do the same analysis for the neural network, we computed the Integrated Gradients (IGs) , with the zero vector as baseline, for each reaction of the Buchwald-Hartwig and Suzuki data sets. Similarly for the random forest, this was done with 1500 labelled data points, which were obtained by using 10 initially labelled data points and utilizing Margin to query labels. The IGs were averaged over all data to obtain a single value for each feature and the value were normalised which is displayed in Table . This provides a way to compare the relative importance of the features. For the Buchwald-Hartwig data, we see that the aryl halide and additive are the most important features which is consistent with the feature importance obtained by the random forest model. For the Suzuki data, reactant 1 seems to be the most important feature when inspecting the average IGs. However, parallels can be drawn between the model feature importance and the explainable variance in a principal component analysis. This allows us to conclude that for the random forest model and complex neural network, two features in the Buchwald-Hartwig dataset accounted for ca. 80% of the prediction outcome with reasonably a good score, which is a good indication that the dataset was easy to learn for both models. On the other hand, the random forest model for the Suzuki dataset does not reach the same level of feature importance even when using the three most important features. As each feature individually plays a larger role -the difference in feature importance outside of the ligand feature is small, and is indicated by the average IGs for the Suzuki datasets. Since a higher dimension space of knowledge is needed to make the decision, it can be concluded that the Suzuki reaction data set is more complex to learn using the observed models. Besides showing that there might be a relation between the models learning the observed space and how well that margin performed, the results of this study imply that there is no drawback to implementing an active learning strategy starting at a small number of labelled data points. There was no observed threshold where the margin query scheme performed worse than a random scheme. As both learning schemes operate within the same space of data, fully trained models should converge towards the same AUROC, at which point it is no longer interesting to use active learning. The benchmarking used in this study focused on minimizing the experimental cost by looking at which point a desired AUROC was met and found an observable advantage for active learning. It is important to note however, that these experiments were computed with a query consisting of a batch of one data point. The experimental designs of full batches of HTE experiments as used in the Buchwald-Hartwig dataset are typically of sizes 96, 384 or 1536. Thus, the results of this study might not be directly translatable to this setup. If analysis time can be reduced to same time-scale of the conducted experiments, the results could be of interest in flow-process design, such as that used in the generation of the Suzuki dataset.