  Modern astronomical surveys are producing datasets of unprecedented size and
richness, increasing the potential for high-impact scientific discovery. This
possibility, coupled with the challenge of exploring a large number of sources,
has led to the development of novel machine-learning-based anomaly detection
approaches, such as Astronomaly. For the first time, we test the scalability of
Astronomaly by applying it to almost 4 million images of galaxies from the Dark
Energy Camera Legacy Survey. We use a trained deep learning algorithm to learn
useful representations of the images and pass these to the anomaly detection
algorithm isolation forest, coupled with Astronomaly's active learning method,
to discover interesting sources. We find that data selection criteria have a
significant impact on the trade-off between finding rare sources such as strong
lenses and introducing artefacts into the dataset. We demonstrate that active
learning is required to identify the most interesting sources and reduce
artefacts, while anomaly detection methods alone are insufficient. Using
Astronomaly, we find 1635 anomalies among the top 2000 sources in the dataset
after applying active learning, including eight strong gravitational lens
candidates, 1609 galaxy merger candidates, and 18 previously unidentified
sources exhibiting highly unusual morphology. Our results show that by
leveraging the human-machine interface, Astronomaly is able to rapidly identify
sources of scientific interest even in large datasets.
