  We present a novel Artificial Intelligence approach for Beyond the Standard
Model parameter space scans by augmenting an Evolutionary Strategy with Novelty
Detection. Our approach leverages the power of Evolutionary Strategies,
previously shown to quickly converge to the valid regions of the parameter
space, with a \emph{novelty reward} to continue exploration once converged.
Taking the $Z_3$ 3HDM as our Physics case, we show how our methodology allows
us to quickly explore highly constrained multidimensional parameter spaces,
providing up to eight orders of magnitude higher sampling efficiency when
compared with pure random sampling and up to four orders of magnitude when
compared to random sampling around the alignment limit. In turn, this enables
us to explore regions of the parameter space that have been hitherto
overlooked, leading to the possibility of novel phenomenological realisations
of the $Z_3$ 3HDM that had not been considered before.
