Relevance feedback can substantially improve retrieval effectiveness. However, the performance depends on the specific documents used; some relevant documents, termed poison pills, may even yield worse performance than that attained without feedback. While prior research has focused on lexical feedback methods and the use of a single feedback document, in this work, we expand the definition of poison pills to sets of documents and study both lexical and neural relevance feedback methods. Experiments across multiple datasets and retrieval approaches show that neural feedback methods achieve higher average effectiveness than lexical methods but are more sensitive to the choice of feedback documents. We further observe notable interactions among feedback documents. Poison pills that degrade performance when used alone can combine to form sets that improve performance. In addition, the performance of combining a relevant document with pseudo-relevant documents depends on whether the relevant document is itself a poison pill.