Score-based structure learning algorithm is commonly used in learning the Bayesian Network. Other than searching strategy, scoring functions play a vital role in these algorithms. Many studies proposed various types of scoring functions with different characteristics. In this study, we compare the performances of five scoring functions: Bayesian Dirichlet equivalent-likelihood (BDe) score (equivalent sample size, ESS of 4 and 10), Akaike Information Criterion (AIC) score, Bayesian Information Criterion (BIC) score and K2 score. Instead of just comparing networks with different scores, we included different learning algorithms to study the relationship between score functions and greedy search learning algorithms. Structural hamming distance is used to measure the difference between networks obtained and the true network. The results are divided into two sections where the first section studies the differences between data with different number of variables and the second section studies the differences between data with different sample sizes. In general, the BIC score performs well and consistently for most data while the BDe score with an equivalent sample size of 4 performs better for data with bigger sample sizes.