  The Evolutionary Map of the Universe (EMU) large-area radio continuum survey
will detect tens of millions of radio galaxies, giving an opportunity for the
detection of previously unknown classes of objects. To maximise the scientific
value and make new discoveries, the analysis of this data will need to go
beyond simple visual inspection. We propose the coarse-grained complexity, a
simple scalar quantity relating to the minimum description length of an image,
that can be used to identify unusual structures. The complexity can be computed
without reference to the broader sample or existing catalogue data, making the
computation efficient on new surveys at very large scales (such as the full EMU
survey). We apply our coarse-grained complexity measure to data from the EMU
Pilot Survey to detect and confirm anomalous objects in this data set and
produce an anomaly catalogue. Rather than work with existing catalogue data
using a specific source detection algorithm, we perform a blind scan of the
area, computing the complexity using a sliding square aperture. The
effectiveness of the complexity measure for identifying anomalous objects is
evaluated using crowd-sourced labels generated via the Zooniverse.org platform.
We find that the complexity scan identifies unusual sources, such as odd radio
circles, by partitioning on complexity. We achieve partitions where 5\% of the
data is estimated to be 86\% complete, and 0.5\% is estimated to be 94\% pure,
with respect to anomalies and use this to produce an anomaly catalogue.
