Recently researchers from Atinary Technologies reported the development of SeMOpt, a BO framework that similarly to MTBO aims to transfer knowledge obtained from previous optimization campaigns to accelerate chemical reaction optimization . In comparison to MTBO, SeMOpt has the advantage of being model agnostic thus can be applied with any combination of surrogate model and acquisition function used during the BO campaign. In addition to the surrogate model used for BO (see Figure ), SeMOpt introduces a surrogate neural process (NP) to model and make predictions based on previously gathered data. Then an acquisition function is used to select likely candidates by evaluating both the surrogate model and NP predictions. SeMOpt introduces the knowledge learnt by biasing the acquisition function of the surrogate model for the target optimization with the acquisition function evaluated on the NP model (Figure ). In addition, the biased introduced to the acquisition function by the NP is continuously updated and decrease as the number of optimization iterations increase. In this way, the optimization surrogate will eventually disregard the bias introduce by the NP whenever it becomes uninformative. The authors benchmarked the performance of the SeMOpt framework by performing an in-silico single-objective optimization of a simulated cross-coupling reaction and a Buchwald Hartwig cross-coupling of aryl halides. For the benchmarking the authors used several different BO algorithms and compared their performance when paired with SeMOpt. The authors observed that for all cases the application of SeMOpt outperformed the single-task implementation of the same BO algorithm. In addition, they compared the performance of SeMOpt against other algorithms that include some knowledge transfer into the optimization workflow including MTBO. The authors observed that SeMOpt outperformed most of the other algorithms, with MTBO closely matching SemMOpt performance.