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In the rapidly evolving field of Artificial Intelligence for Materials Science, algorithmic simplicity is frequently championed as an epistemic virtue, with practitioners prioritizing linear models, shallow architectures, and parsimonious descriptors under the assumption that simpler solutions inherently promote scientific insight and reliability. This paper critically examines the assumption that simplicity is always a scientific virtue in materials AI design, arguing instead that an overemphasis on simplicity introduces high epistemic costs by obscuring the multifaceted, nonlinear, and multi-scale nature of materials phenomena. The analysis unfolds through four interconnected critique points: first, the inherent trade-off between simplicity and predictive accuracy in capturing complex interactions; second, the risk of simplicity functioning as an obscurant that produces misleading yet confident representations; third, the problematic conflation of simplicity with interpretability, where the two concepts are treated as synonymous despite their distinct epistemic roles; and fourth, the fundamental mismatch between simplicity-prioritizing approaches and the intrinsic complexity demanded by real materials systems. These critiques reveal substantial consequences of simplicity bias, including missed opportunities for discovery, underestimation of uncertainty, premature model acceptance, and inefficient research pathways. Ultimately, the paper proposes alternative approaches that embrace appropriate complexity—matching model sophistication to problem demands, employing regularized complexity, leveraging ensemble methods, designing structured complex architectures, and adopting complexity-aware evaluation frameworks—thereby advocating for a more nuanced valuation of model complexity in service of genuine scientific understanding in materials discovery.