The results show that there might be a relation between the machine learning models learning the observed reaction data and how well the margin acquisition function performed. As indicated by the Buchwald-Hartwig dataset, the more accurate the models became, the better query by margin sampling performs. This is consistent with earlier studies showing good results after a certain number of labelled data points. However, we can already observe the effect at ca. 400 labelled data points. This number of queries is relatively large compared to 10 and 100 labelled data points. Consequently, there is no substantial difference in performance when starting with 10 or 100 labelled points. For the Suzuki reaction case it was more difficult for the matrix factorization, random forest and simple neural network models to show a substantial difference between the learning strategies. It was only for the complex neural network that an impact from the Margin acquisition function was observed, by more runs successfully reaching the 0.950 threshold. The AUROC and the performance of the active learning are indications that the Suzuki dataset is more difficult to build an accurate reaction yield prediction model using the studied machine learning algorithms.