  The goal of screening prioritisation in systematic reviews is to identify
relevant documents with high recall and rank them in early positions for
review. This saves reviewing effort if paired with a stopping criterion, and
speeds up review completion if performed alongside downstream tasks. Recent
studies have shown that neural models have good potential on this task, but
their time-consuming fine-tuning and inference discourage their widespread use
for screening prioritisation. In this paper, we propose an alternative approach
that still relies on neural models, but leverages dense representations and
relevance feedback to enhance screening prioritisation, without the need for
costly model fine-tuning and inference. This method exploits continuous
relevance feedback from reviewers during document screening to efficiently
update the dense query representation, which is then applied to rank the
remaining documents to be screened. We evaluate this approach across the CLEF
TAR datasets for this task. Results suggest that the investigated dense
query-driven approach is more efficient than directly using neural models and
shows promising effectiveness compared to previous methods developed on the
considered datasets. Our code is available at
https://github.com/ielab/dense-screening-feedback.
