We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection/reranking. We propose a neural passage selection model that leverages metadata information with a ﬁne-grained encoding strategy, which learns the representation for metadata predicates in a hierarchical way. The models are evaluated on the MS MARCO (Nguyen et al., 2016) and Recipe-MARCO datasets. Results show that our models signiﬁcantly outperform baseline models, which do not incorporate metadata. We also show that the ﬁne-grained encoding’s advantage over other strategies for encoding the metadata.