Active learning 23 is a subset of machine learning that is based on iteratively labeling data points from an unlabeled dataset (in our case, de novo compounds that are built into protein binding pockets and scored). The aim is to pick the most useful samples for training a surrogate model, whilst ultimately minimising the potentially expensive computation needed to find instances that maximise an objective function. There are two main components to an active learning workflow: the regression model, and the acquisition function. Every scored instance is used to train a specified machine learning model, with more examples refining the model accuracy, which is then used to select new molecules to be built. In this work we consider and benchmark two models.