General-purpose text embedding models are trained on broad web-scale corpora, encoding semantic variation across all human knowledge. When applied to retrieval in a narrow domain, most embedding dimensions carry cross-domain noise irrelevant to the task. We investigate whether applying Principal Component Analysis (PCA) to a domain-specific corpus — fitting the projection on document embed- dings alone — recovers a subspace that improves retrieval performance. Using OpenAI text-embedding- 3-small (1536 dimensions) over a 20-topic medical corpus (300 documents, 20 queries), we find that PCA-32 with corpus-only fitting achieves MAP 0.9203 versus a baseline of 0.8750 (+5.2%), while also increasing similarity gap 2.5× and reducing storage 48×. Through five controlled experiments, we show that domain-directed axes are essential (random projection fails), corpus-only PCA fitting outperforms fitting on queries and corpus jointly, and PCA gain increases rather than decreases as corpus diversity grows. Our findings suggest a simple, fine-tuning-free strategy for improving domain-specific retrieval on top of any pre-trained embedding model.