Adequately sampling the populations of local conformational states and understanding the impact of ligand binding on local configurational entropy is critical for accurate free energy predictions. Although rotamer flips may occur on accessible timescales, predicting the true population distribution of a set of sidechain rotamer states still presents challenges, particularly if only a handful of transitions are observed over the course of a simulation. Poor sampling of these transitions can bias free energy calculations used for predicting ligand binding, leading to inaccurate results. Previous studies on T4 lysozyme have demonstrated that insufficient sampling of sidechain motions during simulations for free energy calculations can lead to errors of several kcal/mol. Sidechain rotamer states play a pivotal role in ligand binding. An extensive analysis of a carefully curated library of protein structures in the Protein Data Bank revealed that sidechain rotamer flips in binding sites are both common and critical to binding. Among the >1000 distinct apo/holo pairs evaluated for conformational changes, nearly 90% of those structures undergo at least one rotamer flip in the binding site upon ligand binding; reconfiguration of five or fewer side chains account for 90% of those cases. Here our focus is on accelerating sampling of sidechain rearrangements in proteins. Although conventional Monte Carlo (MC) can facilitate transitions of sidechain rotamers across energy barriers, acceptance rates of such MC move proposals are particularly low for crowded systems. Thus, here, we use non-equilibrium candidate Monte Carlo (NCMC) to improve ac-ceptance of these MC moves. By mixing NCMC sidechain sampling moves with classical molecular dynamics (MD) simulations, we improve sidechain rotamer sampling in solvated systems relative to standard MD.