Finding and describing sub-populations that are exceptional regarding a target propertyhas important applications in many scientific disciplines, from identifyingdisadvantaged demographic groups in census data to finding conductive molecules within gold nanoparticles.Current approaches to finding such subgroups require pre-discretized predictive variables, do not permit non-trivial target distributions, do not scale to large datasets, and struggle to find diverse results. To address these limitations, we propose SYFLOW, an end-to-end optimizable approach in which we leverage normalizing flows to model arbitrary target distributions, and introduce a novel neural layer that results ineasily interpretable subgroup descriptions.We demonstrate on synthetic and real-world data, including a case study,that SYFLOW reliably finds highly exceptional subgroups accompanied by insightful descriptions.