Modern retrieval embeddings are highly compressible, often retaining most of their effectiveness after the removal of a large fraction of dimensions. This suggests that retrieval-relevant information is organized in a lower-dimensional structure, but it remains unclear how broadly that structure generalizes and what ultimately limits compression.

We study this question in long-document financial retrieval where relevant evidence must be distinguished from many highly similar passages containing repeated entities, financial metrics, tables, and boilerplate. Across three financial retrieval benchmarks and eight retrievers, we find that PCA projections learned on one corpus transfer effectively to others, retaining most of the effectiveness of corpus-specific fitting. Transfer quality is strongly associated with principal-subspace overlap, consistent with substantial shared low-dimensional structure across financial corpora. We further show that global geometric measures such as intrinsic dimension only partially explain retrieval robustness under dimensionality reduction. A case study on a financial question answering benchmark links retrieval failures to dense clusters of competing table chunks: Removing these distractors substantially reduces performance degradation. These results suggest that transferability is enabled by low-dimensional structure that generalizes across financial corpora, while compression failures are associated with local retrieval competition that is not captured by existing global geometric measures.