ear Editor, I am pleased to submit our manuscript "Human-Oriented Image Retrieval System (HORSE): A NeuroSymbolic Approach to Optimizing Retrieval of Previewed Images" for consideration in Neurosymbolic Artificial Intelligence. Research Contribution: This work introduces HORSE, a novel neurosymbolic framework that aligns image retrieval with human visual cognition. By integrating cognitive science insights with computational techniques, our system addresses critical limitations in current image search engines that rely on time-consuming preprocessing and struggle with natural language descriptions. Key Innovations: Neuro-symbolic indexing that combines neural networks with symbolic reasoning Human-centric design aligned with visual perception and memory processes Practical applications in design error detection and knowledge management Efficiency gains by moving beyond traditional preprocessing-heavy approaches Relevance to NAI: This research exemplifies neurosymbolic AI by demonstrating how neural-symbolic integration solves real-world perceptual tasks. The work advances theoretical understanding while providing empirical evidence for hybrid approaches in image retrieval, directly contributing to the journal's mission. We believe this manuscript will interest NAI readers working on perceptual AI, human-computer interaction, and practical neurosymbolic applications. The work represents a meaningful step toward more intuitive AI systems that complement human cognitive processes. We confirm this represents original, unpublished work not under consideration elsewhere. All authors have approved the submission. Thank you for your consideration. Sincerely, Dr. Abraham Itzhak Weinberg