Neural network force-fields have enabled molecular dynamics (MD) simulations at unprecedented accuracy by efficiently emulating expensive ab initio calculations. However, these advances have not yet accelerated the long-timescale modelling of biomolecular complexes, where the computational cost of classical force-fields is difficult to reduce. One leading approach for adapting neural network force fields to this context focuses on creating force-fields at a reduced (i.e. coarse-grained) resolution. We here discuss how this task differs from that at the atomistic resolution and discuss recent advances by myself and colleagues which have brought the idea of an accurate and extrapolative neural network protein coarse-grained force-fields within reach, with focus on the collection and processing of training data. Relevant citations: 10.1016/j.sbi.2023.102533 10.48550/arXiv.2310.18278 10.1021/acs.jpclett.3c00444