Graph pattern matching (GPM) is a core primitive in graph analysis with many applications. Efficient processing of GPM on modern NUMA systems poses several challenges, such as an intelligent storage of the graph itself or keeping track of vertex locality information. During query processing, intermediate results need to be communicated, but target partitions are not always directly identifiable, which requires all workers to scan for requested vertices. To optimize this performance bottleneck, we introduce a Bloom filter based workload reduction approach and discuss the benefits and drawbacks of different implementations. Furthermore, we show the trade-offs between invested memory and performance gain, compared to fully redundant storage.