  Mergers play a complex role in galaxy formation and evolution. Continuing to
improve our understanding of these systems require ever larger samples, which
can be difficult (even impossible) to select from individual surveys. We use
the new platform ESA Datalabs to assemble a catalogue of interacting galaxies
from the Hubble Space Telescope science archives; this catalogue is larger than
previously published catalogues by nearly an order of magnitude. In particular,
we apply the Zoobot convolutional neural network directly to the entire public
archive of HST $F814W$ images and make probabilistic interaction predictions
for 126 million sources from the Hubble Source Catalogue. We employ a
combination of automated visual representation and visual analysis to identify
a clean sample of 21,926 interacting galaxy systems, mostly with $z < 1$. Sixty
five percent of these systems have no previous references in either the NASA
Extragalactic Database or Simbad. In the process of removing contamination, we
also discover many other objects of interest, such as gravitational lenses,
edge-on protoplanetary disks, and `backlit' overlapping galaxies. We briefly
investigate the basic properties of this sample, and we make our catalogue
publicly available for use by the community. In addition to providing a new
catalogue of scientifically interesting objects imaged by HST, this work also
demonstrates the power of the ESA Datalabs tool to facilitate substantial
archival analysis without placing a high computational or storage burden on the
end user.
