In general, LSH is great for detecting near-duplicates which are very close, but we have two issues: (1) The stories we're looking for have a greater distance than LSH is great at detecting efficiently and (2) Because our feature vector has structure (named entities, n-grams, etc.) we have problem-specific 'coarse' feature-vector, which is good at detecting possible related candidates cheaply.