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Embedding domain knowledge into materials graph neural networks (GNNs) is not an optional enhancement for performance tuning; it is a fundamental requirement for models that must extrapolate reliably, operate with acceptable sample efficiency, and deliver physically consistent predictions. Purely data-driven architectures, which treat materials systems as generic graphs without built-in physical inductive biases, consistently fail when deployed beyond their training distributions — precisely the regime in which materials discovery operates. This Perspective articulates a clear position: the continued reliance on unconstrained, black-box GNNs represents a dead end for computational materials engineering. Three interlocking reasons compel this stance. First, materials discovery demands extrapolation to unseen compositions, structures, and conditions; unconstrained models offer no guarantees beyond interpolation. Second, density-functional theory (DFT) data remain scarce and computationally prohibitive, rendering sample-inefficient architectures unsustainable. Third, predictions must obey conservation laws, symmetry requirements, and thermodynamic limits; violations render long-term simulations unstable and untrustworthy. Four classes of domain knowledge must be embedded as hard constraints: (1) symmetry (E(3) equivariance, permutation invariance, space-group symmetries), (2) conservation laws (energy conservation, momentum balance), (3) physical scales and units (bounded energies, forces, and lengths), and (4) locality and smoothness principles (finite cutoffs, hierarchical many-body interactions). Recent literature provides compelling evidence that physics-constrained models — notably equivariant architectures — achieve comparable accuracy with an order-of-magnitude reduction in training data while maintaining physical consistency. Conversely, ignoring these constraints leads to extrapolation collapse, massive data waste, unphysical molecular-dynamics drift, and models that cannot be interpreted or transferred. The materials community must therefore treat physics-constrained design as the default, not an afterthought. We recommend concrete standards for model development, benchmark construction, and peer review. Only by making domain knowledge non-optional can machine learning accelerate, rather than merely decorate, the discovery of next-generation materials.