With accurate energies and forces, the opportunity arises to begin to use machine learning based potentials in molecular dynamics simulations of organic molecules, although so far studies have tended to focus on gas phase dynamics. Relatively little is known about the performance of these potentials in the condensed phase, where free energy basins that are unpopulated in the gas phase may emerge. Such considerations are especially important for free energy simulations in computer-aided drug design involving molecules with multiple degrees of freedom where accurate sampling of conformational space is required.