The Nearby Magellanic Clouds system covers 180 square degrees on the sky. Much of it has been mapped across the electromagnetic spectrum at high angular resolution and sensitivity –X-ray (XMM-Newton), UV (UVIT), optical (SMASH), IR (VISTA, WISE, Spitzer, Herschel), radio (ATCA, ASKAP, MeerKAT). This provides us with an excellent dataset to explore the galaxy populations behind the stellar-rich Magellanic Clouds. We seek to identify and characterise AGN via machine learning algorithms on this exquisite data set. Our project focusses not on establishing sequences and distributions of common types of galaxies and active galactic nuclei (AGN), but seeks to identify extreme examples, building on the recent accidental discoveries of unique AGN.