Rank-biased Overlap (RBO) is a measure that is used to compare two rankings against each other mathematically using a hyperparameter for persistence, p, to define the importance of items higher up in the rankings. This is able to follow the properties of incompleteness, indefiniteness, and top-heaviness for its results, making it a flexible option for rank similarity. In traditional RBO, the intersection of the items in each of the rankings is weighted by the persistence to reach the final value for RBO as it tends towards infinity. RBO has several assumptions, such as on what happens when rankings are tied, having an infinitely long ranking, and a degree of conjointness between the rankings. In this paper, two new variations are derived on the aspects of having the rankings be fully conjoint, as well as the aspect of having a known finite domain for the rankings. These are described through the equations of RBOc, for fully conjoint rankings, and RBOf , for rankings within a known finite domain. While RBOc tends to be slightly larger and RBOf tends to be smaller when compared to traditional RBO, both can be more fitting depending on the greater context of their use cases.