This paper describes the KASYS team's participation in the NTCIR-18 SUSHI Task by presenting a multi-level metadata aggregation and retrieval approach for Subtask A, which focuses on retrieving undigitized historical materials with sparse item-level metadata. Our system leverages the hierarchical organization of the data---comprising Box, Folder, and Item levels---by aggregating metadata from lower to higher levels and applying two search strategies (``Merge'' and ``Each''). We evaluate traditional BM25 alongside dense retrieval models (E5 and ColBERT) without fine-tuning, and hyperparameter optimization using Optuna is employed to determine the optimal weight for each level. Although our multi-level score aggregation strategy was designed to exploit the hierarchical structure of the data, it did not yield a significant performance improvement over a simpler BM25 baseline. Future work will explore improved preprocessing of noisy metadata, hybrid retrieval methods combining BM25 with dense re-ranking, and model fine-tuning to further enhance performance in searching undigitized archival collections.