In response to increasing demands for high-performance structural materials, there is a need to develop steels that possess superior combinations of strength and ductility. A promising alloying concept for achieving this are carbide-free bainitic steels. We choose the Fe-C-Si-Mn-Cr-Mo-Al-Mo system, which results in a problem space of 8 dimensions. To effectively query this problem space and finding new chemical compositions, while minimizing the number of need samples and therefore the cost, Bayesian optimization is used in an adapted form. A graphical model is employed as the core of the Bayesian optimization. It embodies a probabilistic process-microstructure-property relationship which allows the integration of physical knowledge. Leveraging physical model, a modular structure is created that can be adapted which evolving knowledge. The dataset combines low-fidelity literature data and high-fidelity characterization data, which includes microstructure details and allows improvement of modules in this structure. We want to present you in this talk this approach to Bayesian optimization. With it, we hope do not only find a better process-microstructure-property-relationship, but also completely novel carbide free bainitic steel compositions.