In this work, we present Surrogate Model-Assisted Molecular Dynamics (SMA-MD), a method for generating equilibrium ensembles of molecules. In SMA-MD, generative models are used to sample a diverse ensemble of initial conditions for short molecular simulations. SMA-MD follows a two-step procedure: First, a generative model mixes efficiently across degrees of freedom which exchange slowly in molecular simulations. Second, we reweight samples against the Boltzmann distribution and run short Molecular Dynamics simulations to equilibrate the local structure and ensure sampling statistics are unbiased with respect to the target Boltzmann distribution. In this manner, SMA-MD is able to capture entropic effects occurring in all degrees of freedom in a molecule, which are critical for the computation of thermodynamic quantities such as free energy differences. We implement SMA-MD using torsional surrogate models and restrict ourselves to working with small non-cyclic molecules. We probe our method by measuring geometric and thermodynamical (potential and implicit solvation free energies) properties and comparing them to the baseline of classic MD simulations, used as the source of training data. We empirically show that our method can generate diverse and physically realistic ensembles. Equilibrium ensembles generated with SMA-MD present higher conformational coverage and lower average energy than those obtained with conventional MD simulations of similar runtime, closely matching long Replica Exchange (RE) simulations.