The Search Marketing team at Amazon manages global search engine traffic to Amazon's website with the aim to optimize long term profitability by delivering the best possible customer experience on Search Engine Results Page (SERPs). In order to do so, we need to run continuous and rapid Search Marketing A/B tests to continuously evolve and improve our products. However, unlike typical e-commerce A/B tests that can randomize based on customer identification, our tests face the challenge of anonymized users on search engines. On the other hand, simply randomizing on products violates Stable Unit Treatment Value Assumption for most treatments of interest. In this work, we propose leveraging censored observational data to construct bipartite (Search Query to Product Ad or Text Ad) SERP interference networks. Using a novel weighting function, we create weighted projections to form unipartite graphs which can then be use to create clusters to randomized on. We demonstrate this experimental design's application in evaluating a new bidding algorithm for Paid Search. Additionally, we provide a blueprint of a novel system architecture utilizing SageMaker which enables polyglot programming to implement each component of the experimental framework.