Margin was compared to random sampling where random unlabelled data points are labelled for each query. Furthermore, we evaluated the active learning setting where a batch of one data point is labelled for each query. We investigated how the different combinations of model, query strategy (either random sampling or margin), and initial pool of labelled data points affect the number of labelled data points required to achieve specific pre-defined levels of AUROC. The pre-defined levels of AUROC were 0.800, 0.850, 0.900 and 0.950.