## 1 Introduction  
The traditional experimental method for screening a large number of molecules towards biological targets is a high-throughput screening technique, but its demand for a large number of resources and time lead to the introduction of computer-aided virtual screening methods to decrease the time and economic costs [1]. The docking programs are used to filter potentially active compounds towards a protein target from a large library of compounds. These docking programs are computationally efficient, fast and of great impact in drug discovery, to search for the favorable position, orientation, and assumed conformation of molecules upon the binding and assigning docking scores to estimate the binding affinity [1, 2, 3, 4, 5]. The major challenge of relying on docking methods to screen a library of molecules against the protein target, is the possibility of providing false binding and ending up eliminating even the potent drug candidates from the list of top hits that will be considered. The reasons for the failure of the docking scores in some instances, are related to their training-set dependencies and scoring-function parameterization [1]. The approach that has been found successful in limiting the possibility of throwing potential drugs into the trash bin of nonactive compounds and achieving a higher success rate, is to combine the results of multiple docking scores and then apply consensus ranking methods [1; 6; 7]. A higher success rate in the virtual screening processes has been obtained by combining results from different docking programs (i.e., consensus scoring) to obtain a final rank [1]. In addition to many traditional consensus methods that have been introduced, which make use of the average scores or the ranking of each molecule from different programs, a new exponential consensus ranking (ECR) was introduced recently that promises a good performance where the traditional consensus fails, due to the poor performance of one or more of the docking programs [1]. This study also employs the consensus docking approach in probing potential antileukemia agents, in combination with LC-MS. The three leukemia targets that are selected in this study are the Fms-like tyrosine kinase 3 (FLT3), B-cell lymphoma-2 (BCL-2), and the mutated BCL-2 G101V. The abnormalities in FLT3, such as the over-expression of the gene or FLT3 mutations from internal tandem duplications (ITDs) within the FLT3 gene are linked to the poor prognosis factors in acute myeloblastic leukemia (AML) and acute lymphoblastic leukemia (ALL) [8; 9; 10]. FLT3 is considered a promising drug target in AML patients with FLT3 mutations [11]. The antileukemia drug that targets FLT3 is gilteritinib, which is known for the treatment of patients with relapsed/refractory FLT3-mutated AML [11; 12]. It is classified as an ATP-competitive type I inhibitor and a selective FLT3 inhibitor that inhibits both FLT3-ITD and FLT3-TKD mutations [11; 13]. BCL-2 is a founding member of the pro-survival class of proteins and functions by interacting with homology 3 (BH3) motifs of pro-apoptotic molecules to restrain its pro-apoptotic function [14]. The overexpression of BCL-2 proteins helps the cancer cells to evade treatment, therefore an effective step toward the treatment of cancers is the downregulation of the anti-apoptotic BCL-2 proteins [15]. Venetoclax is the first BCL-2 antagonist approved, in 2016, for cancer treatment that is targeting the binding groove of BCL-2, that is usually used to interact with BH3 [14; 16]. The binding of venetoclax to BCL-2 will prevent the BCL-2 BH3 protein-protein interaction and then promote the apoptosis in malignant cells, dependent on BCL-2 [14]. The first novel BCL-2 mutation G101V has been found in patients that initially responded to venetoclax treatment, but developed chronic lymphocytic leukemia (CLL) [14; 17].