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Multi-fidelity learning has been positioned as a transformative tool for high-throughput materials screening, offering the ability to fuse inexpensive low-fidelity data—generated from classical force fields, tight-binding models, or CALPHAD thermodynamics—with costly high-fidelity calculations such as density functional theory (DFT). The central premise is straightforward: surrogate models can learn systematic discrepancies between fidelity levels, delivering near-DFT accuracy at a fraction of the computational expense and thereby enabling the rapid exploration of vast compositional and structural spaces that would otherwise remain inaccessible. A substantial body of work in journals including npj Computational Materials, Machine Learning: Science and Technology, Digital Discovery, Journal of Chemical Theory and Computation, Journal of Chemical Physics, and Nature Machine Intelligence has explored this paradigm through linear corrections, Gaussian process discrepancy modeling, neural network transfer learning, multi-fidelity Bayesian optimization, ensemble approaches, and hierarchical correction chains. Yet the promises remain largely unfulfilled. Despite hundreds of reported case studies, the literature reveals few, if any, experimentally validated novel materials discovered solely through multi-fidelity acceleration. Efficiency gains rarely exceed 2–5× in realistic workflows, uncertainty estimates are frequently miscalibrated outside training domains, and extrapolation to chemically distinct regions consistently collapses to the accuracy of the underlying low-fidelity baseline. Overclaims regarding order-of-magnitude cost reductions, guaranteed high-fidelity fidelity, and accelerated discovery have not materialized in practice. This review provides the first systematic synthesis of the 34 peer-reviewed publications that directly address multi-fidelity surrogate strategies in materials screening. It categorizes surrogate architectures, contrasts theoretical assumptions with empirical performance, inventories six recurring broken promises, and distills actionable lessons from the collective evidence. By foregrounding the persistent gaps between theory and application—non-stationary bias, limited transferability, and evaluation practices that favor interpolation over genuine discovery—this work offers a sober roadmap for future research in computational and data-driven materials engineering. The field must move beyond optimistic benchmarks toward honest reporting of real-world discovery metrics if multi-fidelity learning is to fulfill even a modest fraction of its original vision.