  Forthcoming large imaging surveys such as Euclid and the Vera Rubin
Observatory Legacy Survey of Space and Time are expected to find more than
$10^5$ strong gravitational lens systems, including many rare and exotic
populations such as compound lenses, but these $10^5$ systems will be
interspersed among much larger catalogues of $\sim10^9$ galaxies. This volume
of data is too much for visual inspection by volunteers alone to be feasible
and gravitational lenses will only appear in a small fraction of these data
which could cause a large amount of false positives. Machine learning is the
obvious alternative but the algorithms' internal workings are not obviously
interpretable, so their selection functions are opaque and it is not clear
whether they would select against important rare populations. We design, build,
and train several Convolutional Neural Networks (CNNs) to identify strong
gravitational lenses using VIS, Y, J, and H bands of simulated data, with F1
scores between 0.83 and 0.91 on 100,000 test set images. We demonstrate for the
first time that such CNNs do not select against compound lenses, obtaining
recall scores as high as 76\% for compound arcs and 52\% for double rings. We
verify this performance using Hubble Space Telescope (HST) and Hyper
Suprime-Cam (HSC) data of all known compound lens systems. Finally, we explore
for the first time the interpretability of these CNNs using Deep Dream, Guided
Grad-CAM, and by exploring the kernels of the convolutional layers, to
illuminate why CNNs succeed in compound lens selection.
