Molecular dynamics (MD) simulations can complement experiments by providing detailed atomic-level insights into the structure and dynamics of biomolecular condensates, and into the determinants of condensate formation, in terms of the underlying molecular driving forces . However, molecular simulations of biomolecular condensates involve several challenges (Fig. ). First, the sizes of the simulation systems quickly become large (10 5 -10 6 atoms or more), especially if a large number of individual protein and/or nucleic acid molecules are included in the simulations, as is necessary to describe the bulk phase of the condensate and the interface with the surrounding. Second, the timescales of the relevant structural dynamics in the condensates are typically slow (µs -ms or even beyond), and thus long simulation times are needed to sample the motions that govern the conformational ensembles of the constituent biomolecules and their spatial arrangement. Therefore, molecular simulations of biocondensates can be computationally very demanding, especially if atomistic (or all-atom) resolution models with explicit solvent are used (Fig. ). Computationally more efficient coarse-grained (CG) models alleviate this challenge, rendering simulations of large systems on extended timescales computationally tractable (Fig. ). However, accurate energy functions (or "force fields") are of key importance for the simulations to be realistic, in the sense that they provide accurate results and thus can yield the desired molecular-level interpretations of experiments and, possibly, even be used for designing new experiments and predict their outcome. While this is generally true for all force fields, and not neglecting that also the current all-atom force fields still leave room for improvements, the approximations inherent to coarse-graining render the development, parametrization, and validation of CG models particularly important.