Paggi et al. built ComBind on the hypothesis that distinct ligands bind to a receptor in similar ways. We aim to investigate whether this underlying hypothesis of the pipeline is correct by comparing the value of the Open-ComBind objective function for the lowest RMSD ligand poses to the value of the objective when selecting from all gnina generated poses. We only look at target proteins in the Benchmark dataset containing at least ten ligands with a gnina generated near-native pose. In the following experiment, we only use ligands with a defined ground truth pose (i.e. the set of docking ligands for each target receptor in the benchmark dataset) and only ligands that have a gnina generated pose <2 Å RMSD from the ground truth. We first measure the value of the Open-ComBind objective function when we restrict our selection to the best gnina generated pose, according to lowest RMSD to the ground truth. We next evaluate the value of the Open-ComBind objective function when we select from all gnina generated poses of the ligands. If the underlying hypothesis of Open-ComBind is true, then the Open-ComBind objective function value when selecting from the best poses will be greater than or equal to the objective function value when selecting from all gnina generated poses. The Open-ComBind objective is optimized in a stochastic manner, so it is possible we will not converge on the globally optimal pose selections which is assumed to be the set of poses with the lowest RMSD to the ground truth.