Causal discovery from observational and interventional data is challenging due to limited data and non-identifiability which introduces uncertainties in estimating the underlying structural causal model (SCM). Incorporating these uncertainties and selecting optimal experiments (interventions) to perform can help to identify the true SCM faster. Existing methods in experimental design for causal discovery from limited data either rely on linear assumptions for the SCM or select only the intervention target. In this paper, we incorporate recent advances in Bayesian causal discovery into the Bayesian optimal experimental design framework, which allows for active causal discovery of nonlinear, large SCMs, while selecting both the target and the value to intervene with. We demonstrate the performance of the proposed method on synthetic graphs (Erdos-Rènyi, Scale Free) for both linear and nonlinear SCMs as well as on the \emph{in-silico} single-cell gene regulatory network dataset, DREAM.