  We present strong Transformer-based re-ranking and dense retrieval baselines
for the recently released TripClick health ad-hoc retrieval collection. We
improve the - originally too noisy - training data with a simple negative
sampling policy. We achieve large gains over BM25 in the re-ranking task of
TripClick, which were not achieved with the original baselines. Furthermore, we
study 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.
