Understanding customer requirements is a key success factor for both business-to-consumer (B2C) and business-to-business (B2B) enterprises. In a B2C context, most requirements are directly related to products and therefore expressed in keyword-based queries. In comparison, B2B requirements contain more information about customer needs and as such the queries are often in a longer form. Such long-form queries pose significant challenges to the information retrieval task in B2B context. In this work, we address the long-form information retrieval challenges by proposing a combination of (i) traditional retrieval methods, to leverage the lexical match from the query, and (ii) state-of-the-art sentence transformers, to capture the rich context in the long queries. We compare our method against traditional TF-IDF and BM25 models on an internal dataset of 12,368 pairs of long-form requirements and products sold. The evaluation shows promising results and provides directions for future work.