As computers get faster, researchersnot hardware or algorithmsbecome the bottleneck in scientific discovery. Computational study of colloidal self-assembly is one area that is keenly affected: even after computers generate massive amounts of raw data, performing an exhaustive search to determine what (if any) ordered structures occur in a large parameter space of many simulations can be excruciating. We demonstrate how machine learning can be applied to discover interesting areas of parameter space in colloidal self-assembly. We create numerical fingerprintsinspired by bond orientational order diagramsof structures found in self-assembly studies and use these descriptors to both find interesting regions in a phase diagram and identify characteristic local environments in simulations in an automated manner for simple and complex crystal structures. Utilizing these methods allows analysis to keep up with the data generation ability of modern high-throughput computing environments. (c) 2018 American Institute of Chemical Engineers AIChE J, 64: 2198-2206, 2018