Being able to identify regions within or around proteins, to which ligands canpotentially bind, is an essential step to develop new drugs. Binding site iden-tification methods can now profit from the availability of large amounts of 3Dstructures in protein structure databases or from AlphaFold predictions. Currentbinding site identification methods rely on geometric deep learning, which takesgeometric invariances and equivariances into account. Such methods turned outto be very beneficial for physics-related tasks like binding energy or motion tra-jectory prediction. However, their performance at binding site identification isstill limited, which might be due to limited expressivity or oversquashing effectsof E(n)-Equivariant Graph Neural Networks (EGNNs). Here, we extend EGNNsby adding virtual nodes and applying an extended message passing scheme. Thevirtual nodes in these graphs both improve the predictive performance and can alsolearn to represent binding sites. In our experiments, we show that VN-EGNN setsa new state of the art at binding site identification on three common benchmarks,COACH420, HOLO4K, and PDBbind2020.