In this work, we investigate the feasibility of using machine learning in developing accurate representations of the potential energy surface of a flexible, "drug-like" molecule for use in e.g. structure-based lead optimization efforts. We employ the Gaussian Approximation Potential (GAP) 46 framework that is based on a sparse Gaussian process regression technique, which is formally equivalent to kernel ridge regression. 47,48 GAP uses both QM energy and gradient information and although it was originally developed for material systems, it has been used successfully to describe molecular properties. Here we use it to create a potential energy surface for 3-(benzyloxy)pyridin-2-amine (3BPA, Figure ). Although still somewhat smaller than typical drug-like molecules (molecular weight of 200, three rotatable bonds, and three hydrogen bond donors/acceptors), it represents a challenging test case for machine learning due to its internal flexibility. The high effective dimensionality of the configurational manifold of the molecule requires a relatively large amount of training data extensively sampled from the potential energy surface. To address these challenges, we developed an iterative protocol to gradually improve the reconstructed potential, and applied sparsification techniques to cope with the relatively large amount of training data.