We have described a software workflow, AL4GAP, for accelerating the development of machine learning interatomic potentials (ML-IP) via active leaning over a combinatorial composition space. The workflow provides an easy-to-use interface for setting up efficient sampling over arbitrary mixtures of molten salt chemistries. AL4GAP employs low cost empirical forcefields for sampling, and thereby effectively bypassing the need for expensive DFT-MD for training dataset generation. We also provide an easy-to-use interface for the Bayesian optimization of ML-IP model hyperparameters.