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Large area astronomical surveys will almost certainly contain new objects ofa type that have never been seen before. The detection of 'unknown unknowns' byan algorithm is a difficult problem to solve, as unusual things are ofteneasier for a human to spot than a machine. We use the concept of apparentcomplexity, previously applied to detect multi-component radio sources, to scanthe radio continuum Evolutionary Map of the Universe (EMU) Pilot Survey datafor complex and interesting objects in a fully automated and blind manner. Herewe describe how the complexity is defined and measured, how we applied it tothe Pilot Survey data, and how we calibrated the completeness and purity ofthese interesting objects using a crowd-sourced 'zoo'. The results are alsocompared to unexpected and unusual sources already detected in the EMU PilotSurvey, including Odd Radio Circles, that were found by human inspection.

Detecting complex sources in large surveys using an apparent complexity measure