Over the recent years, with the advent of artificial intelligence (AI) methodology, machine learning (ML) approaches saw a rapid adoption in drug discovery. A lot of research has been devoted to constructing artificial neural networks capable of exploring chemical space to suggest novel drug-like candidate molecules for further screens. Deep learning methods have also been successfully applied to predict ligand molecular properties and establishing QSAR models. Establishing structure activity relationships requires accurate prediction of the ligand binding affinity to the target protein and remains a key challenge for computational chemists. Active learning (AL) approaches present a promising pathway to this goal.