As the ZINC and eMolecules stock files are stored in memory, we created a streamlined MolBloom filter that uses optimized hashing techniques to reduce memory usage and increase the processing capacity for stock file checks. After filtering the stock file, the resultant bloom file was less than 1 GB, which allowed over 20 times as many molecules to be run in parallel. A bloom file is a filter which reduces each item into an array. The array contains n items and employs k hash functions to map each element to position k in the array. The purpose is to predict the position of the observed item, instead of storing the position in memory. This method comes with drawbacks, namely, the filter contains a false positive rate of ~1%. As our primary objective was to test the enhancements, false positives should not present problems. However, we anticipate that systematically over-estimates the performance for all the algorithmic variants.