Despite their precision, MLPs still face challenges related to computational cost relative to classical force fields, which can limit their application to smaller systems or shorter simulation timescales of only a few nanoseconds. Accessing large conformational changes in proteins on the scale of thousands of nanoseconds is still not possible with MLPs. In this regard, pragmatic coarse-grained (CG) approaches enable the exploration of large time and length scales while preserving a molecular-level representation of the studied systems. CG potentials have been instrumental in studying large-scale processes such as conformational changes in proteins and the assembly of macromolecular complexes over extended timescales. By integrating the strengths of ML and CG approaches, researchers are working towards hybrid methodologies that balance accuracy with computational efficiency. Indeed, ML techniques can help parametrize CG models, generate CG structures and back-map them to higher resolutions, define optimal parameters and the degree of CG resolution, evaluate the quality of CG mapping, and run CG simulations using ML-based potentials. This synergy holds the potential to overcome existing challenges in biomolecular simulations, allowing for detailed interaction modeling while exploring extensive configurational landscapes.