The Automated Negotiating Agents Competition (ANAC) is an annual competition that compares the state-of-the-art algorithms in the field of automated negotiation. Although in recent years ANAC has given more and more attention to more complex scenarios, the linear and bilateral negotiation domains that were used for its first few editions are still widely used as the default benchmark in automated negotiations research. In this paper, however, we argue that these domains should no longer be used, because they are too simplistic. We demonstrate this with an extremely simple new negotiation strategy called MiCRO, which does not employ any form of opponent modeling or machine learning, but nevertheless outperforms the strongest participants of ANAC 2012, 2013, 2018 and 2019. Furthermore, we provide a theoretical analysis which explains why MiCRO performs so well in the ANAC domains. This analysis may help researchers to design more challenging negotiation domains in the future.