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We describe a method for predicting query difficulty in a precision-oriented web search task. Our approach uses visual features from retrieved surrogate document representations (titles, snippets, etc.) to predict retrieval effectiveness for a query. By training a supervised machine learning algorithm with manually evaluated queries, visual clues indicative of relevance are discovered. We show that this approach has a moderate correlation of 0.57 with precision at 10 scores from manualdoi:10.1145/1076034.1076155 dblp:conf/sigir/JensenBGFC05 fatcat:ck6jsgos4bf6jfkbycjv5kvi4u