Hence, rather than exhaustive or random searches of chemical space, we investigate here the use of active learning to elaborate compound design with FEgrow. The general idea behind this approach is that a subset of compounds is evaluated using an expensive design objective function (in this case the molecular growing and scoring algorithms in FEgrow) and used to train a machine learning model (Figure ). The machine learning model then predicts the objective function for the remainder of the chemical space, and the next subset of molecules is picked for evaluation (for example, in order to optimise the objective or further explore the chemical space). By cycling through this procedure, the algorithm can iteratively make up for any lack of diversity in the initial training subset, and it is has been found previously that the most promising compounds can be identified by evaluating only a fraction of the total chemical space.