Machine learning (ML) has rapidly become a core tool for LHC physics, due to the great volume and complexity of the data that this machine collects. Given that it is not a-priori known what form new physics (if any) might take, there has been a surge of interest in the past year in approaches that would enable an ML algorithm to look for new physics directly in the LHC data without reference to any simulated signal sample. This talk will focus on a concrete example called 'CWoLa Hunting' (Classification Without Labels), in which it assumed that the signal is localized in some window in one variable (e.g. a resonance in an invariant mass) in which the background is smooth, but no additional assumptions are made about the morphology of the signal in some orthogonal set of 'auxiliary' variables. The ML algorithm searches for an unusual population of events in the signal window using these auxiliary variables. I will use as a case study a dijet resonance search in which a resonant signal might form a bump in the dijet invariant mass distribution, while the ML algorithm searches for a localized population of events with unusual jet substructure.