Europeana is the leading digital library of Europe’s cultural heritage, providing access to over 60 million items in more than 40 languages. Its search infrastructure relies on Solr and BM25 over the items’ metadata, thus depending heavily on keyword matching and resource-intensive treatments such as translation and multilingual metadata enrichment. This paper explores the application of Neural Information Retrieval (NIR) approaches in Europeana, focusing on multilinguality. We created a dataset for comparative evaluation, and show that while NIR demonstrates strong potential for multilingual search, challenges remain regarding its performance, particularly for entity-centric queries. This work also highlights the need for more reliable evaluation data.