The next generation of astronomical surveys will revolutionize our understanding of the Universe, raising unprecedented data challenges in the process. One of them is the impossibility to rely on human scanning for the identification of unusual/unpredicted astrophysical objects. Moreover, given that most of the available data will be in the form of photometric observations, such characterization cannot rely on the existence of high resolution spectroscopic observations. We introduce an analysis of anomaly detection in the Open Supernova Catalog (http://sne.space/) with use of machine learning. We developed a strategy and pipeline — where anomalous objects are identified and then submitted to careful individual analysis. This project represents an effective strategy to guarantee we shall not overlook exciting new science hidden in the data we fought so hard to acquire.