Manual expert intervention still constitutes the state-of-the-art in myriad areas, from diagnosing the reasons for a failed reaction to identifying the structure of an unknown analyte. What is common to these and the many other examples discussed here, is the use of information that is external to the immediate problem (i.e., domain knowledge available to the expert) that makes these problems often trivially solvable by expert deduction, but fundamentally unsolvable by typical ML approaches. Research in ML has only indirectly grappled with introducing deductive constraints into models, but solving such problems is arguably the crux of many plans to automate chemical workflows and replace expert interventions. Before broaching the specific challenges of deduction, we will briefly summarize the accomplishments of ML in the chemical sciences to date, and how they ultimately fall short (at least for now) of emulating the expertise of human researchers.