Hudcovic, ThomasRöckl, InesJachmann, JulianZachmann, GabrielCampana, StefanoFerdani, DanieleGraf, HolgerGuidi, GabrieleHegarty, ZackaryPescarin, SofiaRemondino, Fabio2025-09-052025-09-052025978-3-03868-277-6https://doi.org/10.2312/dh.20253256https://diglib.eg.org/handle/10.2312/dh20253256We present a curated multimodal dataset and an accompanying multimodal retrieval system designed to promote reproducible research in art historical information access. The dataset consists of 1,605 digitized photographs of eighteenth-century original prints, with a specific focus on Rocaille ornamentation. Each image is paired with rich metadata as well as additional domain expert commentary. The multimodal retrieval system exposes this corpus through a search engine, implemented with a lightweight architecture. Semantic search is enabled by dense multimodal embeddings. Full-text and fuzzy queries are enabled by conventional database indices. Both types of queries can be easily made through a simple website, exposing the search engine. Our implementation also provides a simple, uniform, queryable REST API, which makes the collection easily and flexibly accessible to researchers with programming skills. Emphasizing scalability and extensibility, the platform can serve as a practical blueprint for deploying multimodal search across specialized image-text datasets. Note that this paper describes work-in-progress; in particular, the multimodal embedding model is currently being implemented.Attribution 4.0 International LicenseCCS Concepts: Applied computing → Fine arts; Information systems → Digital libraries and archives; Multimedia information systems; Search engine architectures and scalability; Computing methodologies → Machine learningApplied computing → Fine artsInformation systems → Digital libraries and archivesMultimedia information systemsSearch engine architectures and scalabilityComputing methodologies → Machine learning10.2312/dh.202532565 pages