For the purpose of minimizing the number of sample model evaluations, we propose and study two algorithms that utilize sequential versions of likelihood-to-evidence ratio neural estimation. We apply our algorithms to a supersymmetric interpretation of the anomalous muon magnetic dipole moment in the context of a phenomenological minimal supersymmetric extension of the standard model, and recover non-trivial models in an experimentally-constrained theory space. Finally we summarize further potential possible uses of these algorithms in future studies.