showing the required number of labelled data points to obtain an AUROC of 0.800, 0.850, 0.900 and 0.950, respectively, for different settings for the Suzuki reaction data set. None of the setting obtained an AUROC of 0.975. Therefore, no figure is displayed for this AUROC for the Suzuki data set. For pre-defined levels of AUROC of 0.800, 0.850 and 0.900, settings with 1000 initial data points reached these levels by only using the initial data points. Thus, these are not shown in the figures. For settings where not all of the 25 runs reached the pre-defined level of AUROC, a number denoting the number of models that succeeded is shown above the respective boxplots. Comparing random sampling and margin, no substantial difference in the required number of labelled data points is seen for an AUROC of 0.800, 0.850 or 0.900. As seen in Figure (d), the complex neural network is the only model that reaches an AUROC of 0.950 at least once for each setting. Moreover, the complex neural network has more runs that reaches the desired AUROC when margin is utilized compared to random sampling. For 10 initial data points, matrix factorization and the complex neural network utilizing random sampling is able to reach an AUROC of 0.950; while random forest and the complex neural network are able to reach this AUROC when utilizing margin. Furthermore, when using 100 initial data points and random sampling, the complex neural network reaches an AUROC 0.950. When utilizing margin, the target is obtained by random forest on five runs, the complex neural network on 23 runs and the simple neural network on one run. For 1000 initial data points and utilizing random sampling, only one run of the complex neural network obtains an AUROC of 0.950; while 20 runs utilizing margin reaches this AUROC.