Three-dimensional protein structures are a key requisite for structure-based drug discovery. For many highly relevant targets, medicinal chemists are confronted with large numbers of target structures in their apo-forms or in complex with a wealth of different ligands. To exploit the full potential of such structure ensembles, in terms of aggregated knowledge that informs design, it is desirable to extract a manageable number of structures that provide a maximum of ligand design opportunities. Most commonly used structure comparison methods are largely based on atom positions and geometry-based metrics; medicinal chemists, however, seek ligand design opportunities and are interested in methods that allow such information to be distilled from structural data and guide them in an intuitive way. Here we present an approach for identifying nonobvious ligand design opportunities in protein conformation ensembles based on the information content in grid maps that represent, for example, binding hotspots. We use four different examples to show how this method can provide information orthogonal to established coordinate-based similarity methods. Furthermore, we demonstrate that ligand design opportunities can change substantially with very small structural variations. We expect that this approach will advance the identification of ligand design opportunities hidden in large collections of protein-ligand complex data that would otherwise have been missed.