Here we have explored different settings of active learning in HTE to better understand when and if active learning can help a model reach a pre-defined level of accuracy faster compared to randomly selecting data points to label. We have done this in a forward prediction setting where reactions are predicted to be either successful or unsuccessful depending on the reaction yield. The rationale for using a binary classification model is that in discovery chemistry a reaction only needs to provide sufficient yield. This is different from process chemistry where the objective is to maximize the yield. In particular, we have investigated the uncertainty-based active learning strategy margin on different machine learning models and different numbers of initially labelled data points in the reaction data set.