• We show that MD-derived models utilize different descriptors compared to models trained on their crystallographic counterparts, pointing to specific features which augment the protein-ligand complex representation. • Generalization potential is greater for the MD derived representation. Both target splits and scaffold splits showed improved results compared to static complex representation.