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We present strong Transformer-based re-ranking and dense retrieval baselinesfor the recently released TripClick health ad-hoc retrieval collection. Weimprove the - originally too noisy - training data with a simple negativesampling policy. We achieve large gains over BM25 in the re-ranking task ofTripClick, which were not achieved with the original baselines. Furthermore, westudy the impact of different domain-specific pre-trained models on TripClick.Finally, we show that dense retrieval outperforms BM25 by considerable margins,even with simple training procedures.

Establishing Strong Baselines for TripClick Health Retrieval