Recent efforts have been made to consider multiple conformations in training GNNs for binding affinity predictions, such as Dynaformer . However, this method utilizes a data augmentation strategy that still relies on individual graphs for each binding conformation, derived from costly MD simulations, to predict affinities. In this work, we introduce DockBox2 (DBX2), a GNNs framework that enables to encode multiple ligand-protein conformations derived from docking within single graphs to leverage ensemble representations, for predicting simultaneously near-to-native binding poses and binding affinities. In a series of retrospective experiments, DBX2 demonstrated significant improved performances both for docking and virtual screening (VS) tasks compared with physics-based and ML methods, warrantying further investigation of ensemble-based ML models in computer-aided drug discovery.