Molecular simulations yield increasingly accurate and chemically detailed insights into the driving forces and molecular interactions underlying liquid-liquid phase separation and the structure and dynamics of biomolecular condensates. While the accuracy of current force fields can be further improved, with machine learning (ML) force fields offering an exciting route [67], the accuracy of today's classical force fields often suffices to provide molecular-level interpretations of experiments, help to design new experiments, and predict their outcome. Quantitative biophysical experiments can provide insights into dynamic and structural aspects of (partially) disordered proteins in condensates, and simulations play an increasingly important role in interpreting such measurements . Taken together, the current developments open the door for simulating increasingly complex and realistic systems and for addressing new questions, in particular if physics-based simulations are synergistically combined with machine learning approaches . In the following, we briefly discuss some possible future directions and challenges.