Many open source tools for each of these steps are now available. For example, for the ML portion (steps 2-5), a manifold of descriptor approaches, ML model architectures, and even active learning tools have been published, each of which excelling in different problem spaces (e.g., data-rich vs data-poor fitting, large-scale vs relatively small-scale simulation, materials vs chemistry applications). Ultimately, this has enabled what were once viewed as challenging fitting problems to serve as basic model and method benchmarks, as well as generation of highly transferable general-purpose ML-IAM parameter sets. However, there remain a number of application spaces necessitating the balance of accuracy and efficiency afforded by ML-IAMs for which model development remains far from trivial. These problems tend to exist at the confluence of high chemical and configurational complexity due to the need for large models, large training sets, and exhaustive validation. In particular, effort to develop ML-IAMs for truly configurationally complex problems (e.g. chemistry in condensed phase molecular systems, phase transformation in covalent materials) has largely been limited to systems of fewer than three atom types, due to the approximately exponential increase in necessary parameters as atomic complexity increases. For example, modeling evolution in carbon-containing systems under reactive conditions is particularly difficult due to the disparate energy scales relevant for conformational change, bond formation/breaking, and non-bonded interactions and the fact that these materials tend to contain three or more element types (e.g., C, N, O, and N). Active learning, in which the fitting framework autonomously attempts to identify maximally informative candidate (i.e., unlabeled) training data for labeling and subsequent addition to the training set can make these fitting problems more tractable by reducing training data volume requirements. However, it does not address the remaining practical challenges of large models, large training sets, and the need for exhaustive validation. Therefore, in this work, we describe a new transfer learning strategy to (1) reduce fit complexity, (2) enable the physicochemical over which models are suited to be better defined, and (3) enable generation of models that are more transferable and efficiently generated than those fit using traditional "direct-learned" methods. Our transfer learning approach is distinct from that used for neural network-type ML-IAMs, and is designed for use with parametrically linear ML-IAMs that employ a cluster-centered (i.e., rather than atom-centered) descriptor. Our strategy draws inspiration from classical transferable force fields, allowing the fit-ting problem to be decomposed into small reusable parameter blocks and thereby enables far greater chemically extensibly than currently available strategies. Our hierarchical strategy also enables immediate application to targeted subsets of chemical space while concurrently refining and expanding model chemical scope. This allows for real-time problem-solving within the initial domain as fitting efforts progressively scale to accommodate increasingly complex chemistry.