In this work, a new hierarchical transfer learning strategy for development of explicitly chemically extensible ML-IAM was demonstrated. Strategy efficacy was evaluated by comparing performance of models fit using the new hierarchical, partial-herarchical, and standard strategies. We provide key findings from this work below, noting that these insights extend beyond the C/N system studied here. In particular, we find that multielement models fit through the standard ChIMES strategy can perform well across a broad range of T , P , and composition. These models can also extrapolate reasonably well to, e.g., single component properties so long as T and P are not at the training limits. The hierarchical training strategy produces models for multielement systems that are as good as those developed using the standard strategy, yet outperform those models for, e.g., single element properties. At the same time, partial hierarchical learning is also effective for cases where pre-existing models exist for some portion of the fitting problem, but for which the user does not wish to fit remaining parameters in multiple separate steps (e.g., like the present Partial model). Ultimately, our new hierarchical transfer learning approach provides a means of breaking down high complexity fitting problems into smaller, more manageable tasks. Critically, this strategy can make ML-IAM development for high complexity systems more accessible to users with limited access to extreme computing infrastructure. Moreover, it also facilitates comprehensive validation, i.e., ensuring that the model will yield quality predictions for all compositions realizable within the hierarchically assembled model space. Future work will investigate whether this strategy can also reduce training data requirements. This was achieved by cloning the ChIMES_Calculator [1] and running the install.sh script located in etc/lmp/.