  Active learning (AL) has interesting features for parameter scans of new
models. We show on a variety of models that AL scans bring large efficiency
gains to the traditionally tedious work of finding boundaries for BSM models.
In the MSSM, this approach produces more accurate bounds. In light of our prior
publication, we further refine the exploration of the parameter space of the
SMSQQ model, and update the maximum mass of a dark matter singlet to 48.4 TeV.
Finally we show that this technique is especially useful in more complex models
like the MDGSSM.
