For these RMSEs to be meaningful, we need to test with ensembles of trajectories which reflect real use cases, and we must know, or have good estimates of, ⟨A⟩ π for observables of interest. This is not straightforward with real simulation data; systems complex enough to be interesting often require long simulations to remove bias and accurately estimate ⟨A⟩ π , and are often expensive to simulate, making the generation of an ensemble of trajectories unfeasible. Cheaper trajectory ensembles can be generated using simpler test systems, such as Chodera's liquid argon, 2 but this does not reflect real use-cases such as protein-ligand binding free energy calculations. Simple models used in the operations research literature have the same problem here. We attempt to address this issue by fitting models to data from compute-intensive absolute binding free energy calculations. From these models, we can cheaply generate ensembles of synthetic trajectories with exactly known properties.