<p>The rapid integration of machine learning (ML) into organic light-emitting diode (OLED) materials design is increasingly constrained by a critical yet under-recognized limitation: the prioritisation of large datasets and complex models at the expense of chemical relevance, physical fidelity, and interpretability. This perspective argues that meaningful progress requires a shift toward a “quality over quantity” framework, prioritising domain-specific molecular representations, physics-informed descriptors, and interpretable models over generic, data-intensive black-box approaches. Emerging evidence substantiates this view. For example, the organic electronic fingerprint (OEFP), designed for conjugated systems, reduces prediction errors by approaching 50% in out-of-distribution tasks compared with conventional descriptors. Similarly, incorporating physically grounded priors, such as triplet excitation energies, bond dissociation energies, and transition dipole moments, improves predictive accuracy while reducing data requirements. In this context, interpretability must be treated as a core design principle rather than a post hoc tool, enabling mechanistic insights that inform rational molecular design. Complementary data-centric approaches, including active learning, transfer learning, and multi-fidelity modelling, further maximize the value of limited high-quality data. By shifting toward chemically informed intelligence and integrating molecular understanding with device and manufacturing constraints, OLED research can develop ML frameworks that are not only predictive but also interpretable and practically transformative for next-generation materials discovery.</p>