  Decays of Higgs boson-like particles into multileptons is a well-motivated
process for investigating physics beyond the Standard Model (SM). A unique
feature of this final state is the precision with which the SM is known. As a
result, simulations are used directly to estimate the background. Current
searches consider specific models and typically focus on those with a single
free parameter to simplify the analysis and interpretation. In this paper, we
explore recent proposals for signal model agnostic searches using machine
learning in the multilepton final state. These tools can be used to
simultaneously search for many models, some of which have no dedicated search
at the Large Hadron Collider. We find that the machine learning methods offer
broad coverage across parameter space beyond where current searches are
sensitive, with a necessary loss of performance compared to dedicated searches
by only about one order of magnitude.
