New deep learning analyses are a promising new method of background rejection and event reconstruction for Imaging Atmospheric Cherenkov Telescopes (IACTs), particularly in the context of the next generation Cherenkov Telescope Array (CTA). This is as they allow for sensitive analysis of complete camera images at high speed. Unlike other fields of astrophysics where deep learning is being used to characterise astronomical sources, deep learning use in IACT astronomy is comparatively unique in that the analysis targets are Extended Air Showers in Earth's atmosphere. As such, we have access to large datasets of highly complex Monte Carlo simulations of both the air shower particle physics and our detectors. However, this in turn leads to a highly non-trivial domain gap problem when attempting to apply deep learning methods trained on simulations to real data. I will present state of the art results displaying the combined effects of custom simulations, Bayesian optimisation and graph-based network architectures to attack this problem.