We describe how the development of advanced materials via high-throughput experimentation at Intermolecular® is accelerated using guidance from modelling, machine learning (ML) and other data-driven approaches. Focusing on rapid development of materials for the semiconductor industry at a reasonable cost, we review the strengths and the limitations of data-driven methods. ML applied to the experimental data accelerates the development of record-breaking materials, but needs a supply of physically meaningful descriptors to succeed in a practical setting. Theoretical materials design greatly benefits from the external modelling ecosystems that have arisen over the last decade, enabling a rapid theoretical screening of materials, including additional material layers introduced to improve the performance of the material stack as a whole, “dopants” to stabilize a given phase of a polymorphic material, etc. We discuss the relative importance of different approaches, and note that the success rates for seemingly similar problems can be drastically different. We then discuss the methods that assist experimentation by providing better phase identification. Finally, we compare the strengths of different approaches, using as an example the problem of identifying regions of thermodynamic stability in multicomponent systems.