In this study we describe AtomNet PoseRanker, a graph-based convolutional neural network trained to identify high-quality crystal-like poses from docking. Importantly, and in contrast to previous work, we use ligand conformations prepared without knowledge of the ligand conformation in the PDB structure, which is representative of use cases encountered in virtual screening applications. Using the curated dataset PDBbind 2019, we demonstrate that our method can improve upon enrichment and ranking metrics for pose tasks compared to the baseline established by the physics-based scoring function vina, as implemented in the opensource docking software Smina. An important aspect of pose prediction in practical use cases for drug discovery campaigns is identifying a biologically relevant and binding-competent receptor conformation. Where there is limited crystallographic information available, such as only a single crystal structure or only a homology model for a target of interest, this can be particularly challenging. We introduce a simple Rosetta-based protocol for focused conformational sampling and demonstrate that docking to small receptor ensembles can improve pose ranking metrics.