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Many domains require analyst expertise to determine what patterns and data are interesting in a corpus. However, most analytics tools attempt to prequalify “interestingness” using algorithmic approaches to provide exploratory overviews. This overview-driven workflow precludes the use of qualitative analysis methodologies in large datasets. This paper discusses a preliminary visual analytics approach demonstrating how visual analytics tools can instead enable expert-driven qualitative analyses at scale by supporting computer-in-the-loop mixed-initiative approaches. We argue that visual analytics tools can support rich qualitative inference by using machine learning methods to continually model and refine what features correlate to an analyst’s on-going qualitative observations and by providing transparency into these features in order to aid analysts in navigating large corpora during qualitative analyses. We illustrate these ideas through an example from social media analysis and discuss open opportunities for designing visualizations that support qualitative inference through computer-in-the-loop approaches.