Figures 4(a)-(c) show boxplots that illustrate the required number of labelled data points to reach an AUROC of 0.800, 0.850 and 0.900 for the Buchwald-Hartwig reaction when either 10 or 100 data points are initially labelled. The settings when 1000 data points were initially labelled reached the target AUROC scores using only the initially labelled data points and are, therefore, not shown in the figures. All 25 runs obtained an AUROC of 0.800, 0.850 and 0.900. When utilizing complex and simple neural network with the Buchwald-Hartwig data, starting with 10 randomly labelled data points seems to require a lower number of labelled data points to achieve an AUROC of 0.800 or 0.850. This is compared to starting with 100 randomly labelled data points. Matrix factorization with 10 initial data points shows a lower number of required labelled data points, compared to starting with 100 initial data points to achieve the target AUROC. No substantial difference is displayed when utilizing random sampling compared to margin to obtain an AUROC of 0.800 and 0.850. For a pre-defined target AUROC of 0.900, utilizing margin require a lower number of labelled data points when using random forest. Figures -(e) display boxplots of the required number of labelled data points to reach an AUROC of 0.950 and 0.975, respectively, when either 10, 100 or 1000 data points are initially labelled. The numbers above the boxplots show the number of runs that reach the AUROC if not all runs did. Both figures show a lower number of required labelled data points when using Margin with 10 or 100 initial data points, compared to using random sampling. When utilizing margin with 1000 initial data points, more runs reach the AUROC of 0.975 compared to the runs when random sampling was used. initially labelled data points, all models reached the AUROC of 0.800, 0.850 and 0.900 by using only the initial labels and, therefore, these models are not shown.