Monitoring climate change adaptation (CCA) in the heritage sector, especially in the context of World Heritage (WH) management, requires systematic analyses of policy documents for CCA strategies, a process that is difficult to scale. We ask whether automated retrieval systems can prioritise expert annotation effort: given a CCA concept, can a system rank passages of a management plan so that those an expert would annotate appear near the top, without any in-domain supervision? This objective is formulated as an information retrieval problem with a new benchmark dataset on climate-adaptive heritage management strategies, created from expert-labelled text chunks in official Management Plans of 11 Dutch WH properties. Four retrieval approaches (BM25, a bi-encoder, a dense retriever with reranker, and a cross-encoder reranker) are evaluated and compared. The cross-encoder reranker achieves the strongest performance across labels. An LLM-as-judge evaluation further shows that top-ranked unlabelled chunks could be relevant, suggesting that the existing annotations are precise but not exhaustive. Per-label analysis identifies which concepts are automatically retrievable and which remain beyond the reach of general-purpose retrieval. Our benchmark establishes a foundation for scalable mapping and monitoring of climate change adaptation in the context of heritage management.